Context as ontology and epistemic infrastructure: Rethinking explanation in economic geography 285DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. Introduction Explanation has long been a central concern in economic geography. Since the critiques of the quantitative revolution, scholars have ques- tioned whether correlations between variables can provide sufficient grounds for causal un- derstanding (Harvey, D. 1969; Sayer, A. 1984). The debate has re-emerged in recent years as big data, spatial econometrics, and machine learning have been mobilised to identify patterns of clustering, diffusion, or associa- tion (Kitchin, R. 2014; Shelton, T. et al. 2015; Arribas‐Bel, D. and Reades, J. 2018). These tools provide new descriptive and predictive capacities, but they also risk reducing expla- nation to statistically robust regularities. It is now widely recognised across the social sci- ences that statistical correlation does not by itself provide causal explanation. The chal- lenge, as emphasised by Hedström, P. and Swedberg, R. (1998) and Elster, J. (2015), lies 1 Faculty of Geography, Babeș‐Bolyai University. 5–7 Clinicilor Street, 400006 Cluj‐Napoca, Romania. E‐mails: jozsef.benedek@ubbcluj.ro; andreea.toiu@ubbcluj.ro 2 Budapest Metropolitan University. Nagy Lajos király útja 1–9. 1148 Budapest, Hungary. Context as ontology and epistemic infrastructure: Rethinking explanation in economic geography József BENEDEK1,2 and Andreea ŢOIU1 Abstract This paper clarifies how mechanism‐based explanation can work in economic geography when digital spatial methods are routine. We outline a critical realist orientation that treats socio-spatial context in two linked ways: as an ontological condition that enables or constrains causal powers, and as an epistemic infrastructure that organises the categories through which mechanisms become visible. On this basis, explanation involves speci- fying mechanisms, scope conditions, and likely empirical traces, while attending to how data systems shape what can be observed. We illustrate the approach with two short cases from Romania. First, spatial models of COVID‐19 vaccine uptake identify clustering and diffusion, but explanation arises only when these patterns are situated within a layered health regime shaped by socialist legacies, market reforms, and transnational guidance. Second, typologies of peri-urban change derived from demographic and satellite data are read as traces of spatial figurations generated by property restitution, fragmented planning, and capital flows. In both cases, the same variables can sustain divergent ontological commitments: mechanisms treated as regularities, or mechanisms identified as generative structures with stated conditions of activation. The paper’s contribu- tion is practical. It offers a clear statement of the framework, two heuristic illustrations that connect patterns to mechanisms, and a set of design suggestions: state mechanisms and scope before methods; use digital tools to locate and evaluate traces rather than to stand in for mechanisms; combine quantitative outputs with institu- tional and historical evidence; and document the fit of travelling categories to regional ontologies. We do not claim to settle the debate. Our aim is to show how explanation can proceed in a way that is transparent about assumptions and proportional in its claims. Viewed this way, the paper provides a tractable starting point for cumulative, comparative, theory‐building research in and beyond Central and Eastern Europe. Keywords: mechanism-based explanation, critical realism, geography of knowledge, digital spatial technologies, Central and Eastern Europe Received June 2025, accepted September 2025. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299.286 in identifying the mechanisms that generate the observed associations. Critical realism has been one of the most sustained philosophical resources for rethinking explanation in geography, empha- sising that mechanisms are real causal pow- ers which operate contingently under ena- bling and constraining conditions (Bhaskar, R. 1979; Sayer, R.A. 1992). From Bhaskar’s foundational claims about a stratified ontol- ogy (1975, 1979) to Sayer’s influential work in geography (1992, 2010), critical realism has emphasised that mechanisms are real causal powers which operate contingently, depend- ing on enabling and constraining conditions. Early interventions introduced this orienta- tion into economic geography (Johnston, R. 1992; Pratt, A.C. 1995; Yeung, H.W. 1997), insisting that explanatory depth could not be achieved by correlation alone. More recent contributions, such as Yeung, H.W. (2019, 2023), have reformulated this agenda as an explanatory realism, where mid-range theorising specifies mechanisms, scope con- ditions, and empirical traces while accom- modating epistemic pluralism. A central implication of this approach is that ontological commitments shape epis- temological categories and methodological practices. Structures at the level of the real generate practical ontologies, which in turn condition how actors and scientists perceive problems and mobilise categories of investi- gation (Bhaskar, R. 1979; Yeung, H.W. 2023). The geography of knowledge tradition has long emphasised that categories of analysis travel across regions, often obscuring local generative structures (Livingstone, D.N. 2013; Meusburger, P. et al. 2018). For exam- ple, Anglo-American concepts of govern- ance, neoliberalisation, or urban resilience have often been imported into Central and Eastern Europe (CEE), where they flatten a stratified regional ontology into empirical anomalies or derivative cases (Stenning, A. 2005; Müller, M. 2019; Nagy, E. 2025). This recursive relation between ontology, epis- temology, and methodology is essential for producing adequate explanations. At the same time, debates about digital spa- tial technologies have further complicated the relationship between theory, ontology, and method. Kitchin, R. (2014, 2022) challenged claims about the “end of theory” in big data, showing that data are always theory-laden and embedded in socio-technical infrastruc- tures. Thatcher, J. et al. (2016) conceptualised “data colonialism,” highlighting how digital infrastructures extract, commodify, and cen- tralise data in ways that reproduce long-stand- ing inequalities. More recent work has shown how artificial intelligence, machine learning, and remote sensing embed epistemic assump- tions that shape what is visible, measurable, and explainable in space (Dalton, C.M. and Thatcher, J. 2015; Lynch, M. 2022). These cri- tiques converge with critical realist concerns: data infrastructures are not neutral but condi- tion explanatory claims by embedding power relations and epistemic categories. This article contributes to the latest aca- demic discourse on the role of theory in geographical explanation launched by the recently published contribution of Henry Yeung. More exactly the paper addresses a key issue of Yeung’s conception on theory building, namely the role of context. Our arguments are novel and original in the sense that we address the question of context from a twofold perspective: one is offered by the latest technological advancements in data processing (geospatial technologies) and the second is represented by the spe- cific central‐eastern European perspective. The paper argues that socio-spatial context should be conceptualised in economic geog- raphy not only as an ontological condition for mechanism activation but also as an epis- temic infrastructure. Ontologically, mecha- nisms operate only in stratified contexts shaped by institutional legacies, multi-sca- lar governance, and material infrastructures. Epistemically, the categories used to identify mechanisms are themselves conditioned by regional ontologies and by the circulation of epistemologies across academic communi- ties. Without attending to both dimensions, mechanism-based explanation risks falling 287DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. This perspective also carries implications for how digital spatial technologies are incor- porated into research design. While GIS (Geographic information system), remote sensing, and spatial econometrics can enrich explanation by identifying clusters, spill- overs, or diffusion effects, their contribution depends on whether they are embedded in theory-led approaches. Without theoretical framing, they risk collapsing into correla- tionism, treating observed regularities as mechanisms in themselves. With theoretical framing, they can provide empirical traces that help identify generative structures. As Wyly, E. (2011) and Dodgson, M. et al. (2014) argue, quantitative methods can be repur- posed for realist ends if they are aligned with ontological commitments and used to specify scope conditions. To substantiate this argument, the paper presents two empirical illustrations. The first concerns vaccine uptake in Romania, where the same dataset has been mobilised in two different ways: once through spatial econometric modelling of clustering and dif- fusion Mare, C. et al. (2024) and once through theorisation of hybrid health regimes com- bining socialist legacies, neoliberal reforms, and global governance Petrovici, N. et al. (2023). The second concerns peri-urbanisa- tion, where demographic and satellite data have been used to typologise post-socialist cities as cases of growth and decline (Sandu, A. 2024), but also to theorise “spatial figura- tions” as stratified outcomes of institutional layering and capital flows (Petrovici, N. and Poenaru, F. 2025). In both cases, the same variables yield flat, correlationist explana- tions under a positivist ontology, or strati- fied, mechanism‐based explanations under a critical realist ontology. The contribution of the paper is threefold. Conceptually, we clarify and operationalise a mechanism-based approach that treats socio-spatial context as both an ontological condition and an epistemic infrastructure. Methodologically, we set out research‐de- sign principles for integrating digital spatial methods into mechanism-oriented inquiry into two extremes: abstract universalism, which assumes mechanisms travel every- where without modification, or local excep- tionalism, which isolates cases without theo- rising their generative mechanisms. The argument develops in dialogue with Yeung, H.W. (2023) call for explanatory real- ism but extends it in two ways. First, we emphasise that practical ontologies emerg- ing from social structures can transform the epistemic categories of actors, including scien- tists. This recursive relation between ontology and epistemology changes both categories of perception and categories of investigation. Second, we draw on the geography of knowl- edge tradition to argue that the circulation of concepts across regions can obscure or reveal local generative structures, thereby producing emergent epistemic effects (Livingstone, D.N. and Withers, C.W.J. 2011; Meusburger, P. et al. 2018; Paasi, A. 2025). In this sense, context is both ontological and epistemic: it shapes the activation of mechanisms and the categories through which mechanisms are rendered intelligible. The implications of this perspective can be demonstrated through Central and Eastern Europe, a region that has repeatedly been cast as derivative or exceptional in economic geog- raphy. Post-socialist transformations have produced structured variation in institutional capacity, governance models, and socio-spa- tial outcomes. Countries across the region lib- eralised markets, decentralised governance, and integrated into European and global economies, yet outcomes diverged markedly in areas such as foreign investment, innova- tion, and urban development (Pickles, J. 2010; Smith, A. and Timár, J. 2010). More recent work has argued that these divergences reflect not anomalies but the operation of hybrid and layered mechanisms that combine socialist legacies, neoliberal reforms, and global insti- tutional pressures (Pucherová, D. and Gáfrik, R. 2015; Müller, M. 2019; McElroy, E. and Chelcea, L. 2025). Treating CEE as an onto- logically stratified region therefore reveals how socio-spatial context generates mecha- nisms of wider theoretical significance. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299.288 by specifying mechanisms and scope con- ditions in advance, using digital outputs as empirical traces, and documenting the fit of travelling categories to regional ontolo- gies. Empirically, we show that Central and Eastern Europe is not a residue of anoma- lous data but a region where hybrid insti- tutions and epistemic effects make visible the recursive relation between ontology and knowledge production. More broadly, the paper contributes to debates on the role of theory in economic geography (Barnes, T.J. and Christophers, B. 2018; Rodríguez‐Pose, A. 2021), the continuing relevance of post-so- cialist studies (Müller, M. 2019; McElroy, E. and Chelcea, L. 2025), and the integration of digital spatial technologies into explana- tory research (Arribas‐Bel, D. and Reades, J. 2018; Ash, J. et al. 2018; Kitchin, R. 2022). The paper proceeds as follows. Section 2 traces the genealogies of mechanism-based explanation in geography, from early cri- tiques of positivism to recent calls for explanatory realism. Section 3 develops the dual framing of socio-spatial context as both ontological condition and epistemic infra- structure. Section 4 considers how digital spatial technologies can be integrated into theory-led mechanism design. Section 5 pres- ents empirical illustrations from CEE. Section 6 concludes with implications for advancing context-sensitive, mechanism-based explana- tion in economic geography. Genealogies of mechanism-based explanation in geography The search for causal explanation in geogra- phy has unfolded through successive phases of critique, reformulation, and methodological experimentation. The first decisive break came with the critique of the quantitative revolution. David Harvey’s Explanation in Geography (1969) reflected the ambition to construct nomothetic science through statistical laws, but it also re- vealed the fragility of reducing explanation to correlations. By the late 1970s, critical interven- tions (Harvey, D. 1969; Wisner, B. 1978; Soja, E.W. 1980) highlighted how spatial-economic patterns could not be understood without ref- erence to political economy, class relations, and power. These early critiques already antici- pated the call for mechanism-based reasoning, since they questioned whether universal laws were feasible in open social systems. Realist philosophy provided a systematic alternative. Bhaskar, R. (1975) introduced the notion of a stratified ontology, distin- guishing the real (generative structures), the actual (events), and the empirical (observa- tions). Sayer, R.A. (1984, 1992) adapted these insights into geography, insisting that expla- nation required uncovering mechanisms operating under contingent conditions, not just observable regularities. Johnston, R. (1992) pressed this critique further by empha- sising that the closure assumed in positivist models was incompatible with the openness of social systems. Pratt, A.C. (1995) and Yeung, H.W. (1997) made these philosophical principles operational for economic geogra- phy: mechanisms should be traced through comparative strategies, mixed methods, and multi-scalar analysis. During the 2000s, empirical work demon- strated the potential of this approach. Glasmeier, A.K. and Farrigan, T.L. (2007) showed how urban segregation and eco- nomic isolation emerge from the contin- gent interplay of labour markets, housing institutions, and racialised practices, rather than from single-variable correlations. Evolutionary economic geography (Boschma, R.A. and Frenken, K. 2006; Boschma, R.A. and Martin, R. 2010; Clark, G.L. et al. 2018) proposed a mechanism-oriented account of regional development, where related variety, branching, and path dependence were not abstract models but causal processes embed- ded in institutional contexts. These contri- butions also aligned with broader meth- odological debates in social science, where Hedström, P. and Swedberg, R. (1998) and Elster, J. (2015) promoted mechanism-based explanation and mid-range theorising. A recurrent ambiguity has concerned the relation between mechanisms and processes. 289DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. While often used interchangeably, critical realist accounts distinguish between them. Processes denote sequences of events observ- able at the empirical and actual levels, while mechanisms refer to the generative structures that make such processes possible (Sayer, A. 2002; Yeung, H.W. (1997). For example, “urban sprawl” may appear as a general pro- cess across contexts, but the mechanisms pro- ducing it differ in the United States through suburban property regimes, in post-social- ist Europe through restitution policies and fragmented planning systems (Stenning, A. 2005; Hirt, S.A, 2012). Mechanism‐based explanation, thus, requires moving beyond descriptive process‐tracing to the identifica- tion of causal powers activated under specific socio-spatial conditions. The 2010s brought confrontation with the digital turn. Wyly, E. (2011) asked whether quantitative tools could be repurposed for radical ends when re-embedded in realist ontology. Kitchin, R. (2014) dismantled the rhetoric of the “end of theory” showing how data are always theory-laden and embedded in socio-technical infrastructures. Thatcher, J. et al. (2016) conceptualised “data colonial- ism” as a mode of dispossession, linking the epistemic power of digital infrastruc- tures to broader geographies of inequality. Critical GIS scholarship reinforced these insights: Pickles, J. (1995), Schuurman, N. (2000), Goodchild, M.F. (2007), and Ash, J. et al. (2018) demonstrated that spatial data infrastructures are not neutral but privilege certain ways of knowing, thereby shaping which mechanisms can be rendered visible. More recent debates have returned explic- itly to the methodological core. MacLeavy, J. (2019) argued that in open systems the dis- tinction between mechanisms, processes, and contexts cannot be neatly separated. Crespi, F. and Quatraro, F. (2015) insisted that mecha- nisms are never universal but conditional on institutional and spatial settings. Dodgson, M. et al. (2014) applied this reasoning to innovation ecosystems, where non-linear and multi-scalar interactions require mech- anism‐based explanations attentive to com- plexity. Yeung, H.W. (2019, 2023), reformu- lated this orientation as “explanatory realism” a pragmatic stance where mid-range theories identify mechanisms and scope conditions, while recognising epistemic pluralism. Since 2020, further contributions have underscored both the opportunities and the risks of mechanism-based explanation. Ash, J. et al. (2018) called for moving beyond cri- tique to reconstruct explanatory practices, while Lynch, M. (2022) examined how data infrastructures codify particular epistemolo- gies of space. Paasi, A. (2025) extended these debates into regional theory, showing that spatial categories themselves are ontologi- cal constructions shaping how mechanisms are identified. At the same time, the geog- raphy of knowledge tradition (Livingstone, D.N. 2013; Meusburger, P. et al. 2018) high- lights the recursive relation between ontol- ogy, epistemology, and methodology: real structures generate practical ontologies that condition how actors and scientists perceive problems and mobilise categories of investi- gation (Bhaskar, R. 1979; Yeung, H.W. 2023). This issue is particularly salient in post-so- cialist studies. Imported epistemologies often flatten stratified regional ontologies into derivative anomalies, reducing CEE to a site of empirical irregularities rather than a source of theory (Pucherová, D. and Gáfrik, R. 2015; Müller, M. 2019; Nagy, E. 2025; McElroy, E. and Chelcea, L. 2025). By contrast, mechanism-based reasoning allows treating the region as a generative site of the- ory production, where institutional hybrid- ity and layered sovereignties create mecha- nisms of wider relevance (Stenning, A. 2005; Petrovici, N. 2012). Taken together, this genealogy charts a trajectory from the critique of positivism, through the adoption of realist philosophy, to methodological embedding and contem- porary debates about digital epistemologies. The unifying thread is a persistent concern with context: mechanisms operate contin- gently in open systems, and explanatory ade- quacy requires both ontological specification and epistemic reflexivity Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299.290 Socio-spatial context as ontological condition and epistemic infrastructure In order to advance mechanism-based ex- planation, socio-spatial context must be analysed in two complementary ways: as an ontological condition that enables or con- strains the operation of mechanisms, and as an epistemic infrastructure that frames the categories through which mechanisms are identified. Both perspectives are necessary if causal explanation in economic geography is to move beyond the limits of either abstract universalism or local exceptionalism. Ontological conditions refer to the institu- tional, political, and material structures that shape the environments in which mecha- nisms are activated. Critical realism empha- sises that mechanisms are real causal powers, but their effects depend on the stratified con- texts in which they are embedded (Bhaskar, R. 1975; Sayer, R.A. 1992). Comparative research has shown how similar processes yield divergent outcomes under different institutional arrangements. For example, foreign direct investment generates dis- tinct developmental trajectories depending on whether states exercise strategic coor- dination or rely on liberal market regimes (Pickles, J. 2010; Smith, A. and Timár, J. 2010). Evolutionary economic geography has further demonstrated that path depen- dence, related variety, and branching operate through concrete industrial structures and governance systems rather than as univer- sal processes (Boschma, R.A. and Martin, R. 2010; Balland, P.-A. et al. 2019). These studies illustrate that the explanatory power of mechanisms derives not only from their existence but also from their embedding in particular socio-spatial conditions. Epistemic infrastructures concern the frameworks of knowledge through which mechanisms are rendered visible. Categories of investigation are not neutral descriptors but emerge within scholarly traditions, data practices, and institutional routines (Livingstone, D.N. 2013; Meusburger, P. et al. 2018). What counts as a valid mechanism is shaped by epistemological assumptions embedded in these infrastructures. For exam- ple, the circulation of Anglo-American con- cepts of governance or neoliberalisation into post-socialist contexts has often obscured the specific institutional legacies of the region, reclassifying them as anomalies instead of potential sources of explanation (Stenning, A. 2005; Müller, M. 2019; Nagy, E. 2025). Recent debates highlight that epistemic infrastructures are themselves productive: they generate categories that shape empirical research and theory formation (Barnes, T.J. and Christophers, B. 2018; Paasi, A 2025). Recognising this role is crucial for assessing how knowledge practices enable or constrain mechanism identification. The interaction between ontological and epistemic dimensions is recursive. Real struc- tures generate practical ontologies that influ- ence how social actors and scientists perceive and categorise problems (Bhaskar, R. 1979). These categories, once institutionalised in research practices, shape subsequent inves- tigations, determining how mechanisms are conceptualised and tested. Yeung, H.W. (2023) reformulates this relationship within his framework of explanatory realism, arguing that mid‐range theorising must remain reflex- ive about the epistemic assumptions that guide mechanism identification. Contributions from the geography of knowledge have reinforced this argument by showing that categories travelling across regions generate emergent epistemic effects when applied in new con- texts (Livingstone, D.N. and Withers, C.W.J. 2011; Jessop, B. and Sum, N.‐L. 2022). Adequate explanation therefore requires attention both to the structural conditions that activate mech- anisms and to the epistemic infrastructures that make them intelligible. This dual framing is particularly signifi- cant for post-socialist studies. The region has often been interpreted through concepts that position it as derivative of Western trajectories or as an empirical exception. Imported categories such as “transition” or “convergence” have sometimes flattened the stratified institutional landscape of Central 291DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. and Eastern Europe into anomalies, thereby obscuring the generative mechanisms at work (Pucherová, D. and Gáfrik, R. 2015; Müller, M. 2019; Nagy, E. 2025). Treating CEE instead as an ontologically stratified formation highlights how socialist legacies, neoliberal reforms, and global integration interact to create hybrid mechanisms that cannot be reduced to exceptions. At the same time, recognising the epistemic infra- structures through which categories travel sheds light on how external concepts shape the types of explanations that are legitimised. Recent interventions argue that post-social- ism continues to serve as a site of theory production when analysed as an interaction between institutional layering and epistemic circulation rather than as a residual descrip- tive label (McElroy, E. and Chelcea, L. 2025; Kinossian, N. 2022). This perspective aligns with the broader call to treat regional ontol- ogies as sources of explanatory innovation rather than as deviations from supposedly universal models. Attention to both ontological and epis- temic dimensions is also essential in relation to digital spatial technologies. Tools such as GIS, remote sensing, and spatial economet- rics can provide valuable empirical traces of clustering, diffusion, or association. Yet these traces contribute to causal explanation only when interpreted within theory-led designs that identify the causal mechanisms involved (Wyly, E. 2011; Dodgson, M. et al. 2014; Kitchen, R. 2022). Without such embedding, digital methods risk reproducing correlation- ism, treating observed regularities as mecha- nisms in themselves. With theoretical framing, however, they can support mechanism-based explanation by situating empirical observa- tions within stratified socio‐spatial contexts. Recent work on artificial intelligence and machine learning demonstrates this tension: while these methods can uncover patterns at multiple scales, their explanatory value depends on whether results are incorporated into mechanism-oriented accounts of spatial processes (Ash, J. et al. 2018; Shelton, T. 2024). Digital infrastructures therefore exemplify how ontological and epistemic dimensions intersect: the data they produce are condi- tioned by socio-technical structures, while the categories through which they are mobilised shape explanatory outcomes. Taken together, these points indicate that socio-spatial context must be treated along two linked dimensions. As an ontological con- dition, it sets the enabling and constraining environment in which mechanisms operate. As an epistemic infrastructure, it organises the categories and practices through which mechanisms are made legible. Attending to both avoids the twin errors of universalism and exceptionalism. For Central and Eastern Europe, this means tracing how institutional legacies meet circulating epistemologies and how this encounter shapes the identification of mechanisms whose scope and limits can be specified beyond the region. Digital spatial technologies and mechanism-based explanation The expansion of digital spatial technologies has altered both the empirical possibilities and the epistemological challenges of expla- nation in economic geography. GIS, spatial econometrics, remote sensing, and, more recently, artificial intelligence and machine learning, have been promoted as offering unprecedented capacity to capture spatial regularities, identify clusters, and model diffusion processes (Goodchild, M.F. 2007; Kitchen, R. 2014, 2022; Arribas‐Bel, D. and Reades, J. 2018). These tools provide de- scriptive power at large scales and across multiple dimensions of socio-spatial life. Yet their contribution to causal explanation depends on whether they are embedded within theory-led research designs. Without theoretical framing, they risk reproducing correlationism in a new guise, substituting pattern detection for identification of genera- tive mechanisms (Wyly, E. 2011). Critical realist perspectives highlight that data do not speak for themselves but must be situated within an ontology that distin- Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299.292 guishes between events, mechanisms, and structures (Bhaskar, R. 1975; Sayer, R.A. 1992). From this standpoint, digital traces can serve as empirical evidence of causal pro- cesses, but they cannot define those processes without theory. Data infrastructures also embed assumptions about what counts as a valid observation. Lynch, M. (2022) further argues that the digitalisation of geography embeds new power relations into explana- tory practices by privileging what is visible and measurable over what is institutionally or socially latent. Mechanism‐based reason- ing requires treating these outputs as poten- tial empirical traces of deeper structures rather than as explanations in themselves. The tension is especially evident in appli- cations of spatial econometrics and machine learning. Models of autocorrelation, cluster- ing, or diffusion identify patterns across ter- ritorial units, but they do not by themselves reveal why certain outcomes occur. For exam- ple, clustering of foreign direct investment in specific regions may reflect the operation of multiple mechanisms, including state industrial policy, labour market institutions, and global production networks. Only com- parative and historically grounded analysis can disentangle which mechanisms are acti- vated under particular conditions (Boschma, R.A. and Martin, R. 2010; Pickles, J. 2010). Machine learning techniques that classify urban growth trajectories or predict house- hold mobility likewise risk producing corre- lationist explanations unless their results are situated within mechanism-oriented accounts of urban governance, land regimes, or infra- structure development (Shelton, T. 2024). Digital technologies also shape epistemic infrastructures by defining categories of analysis. Remote sensing data, for instance, classify land cover and land use according to global taxonomies, often obscuring local institutional meanings. Similarly, the use of “standard” econometric indicators of regional competitiveness imports categories devel- oped in Western economies into post-social- ist settings, potentially reinterpreting insti- tutional hybridity as deviation or anomaly (Stenning, A. 2005; Müller, M. 2019; Nagy, E. 2025). In this sense, digital infrastructures exemplify how epistemological categories travel and are institutionalised, influencing which mechanisms can be identified. Paasi, A. (2025) has argued that spatial categories are themselves ontological constructions that condition explanatory reasoning; when embedded in digital platforms, these catego- ries carry strong epistemic effects. Central and Eastern Europe illustrates both the opportunities and the risks of digi- tal methods for mechanism-based expla- nation. In the field of public health, spatial econometric models of vaccine uptake in Romania identified clustering and diffu- sion patterns across counties (Mare, C. et al. 2024). While such models capture empirical regularities, they do not specify why uptake diverged across similar institutional environ- ments. A mechanism-based account situates these patterns within the layered health regime shaped by socialist legacies, neolib- eral reforms, and transnational governance (Petrovici, N. et al. 2023). In this case, digital tools provide valuable traces, but explanation requires theorising the institutional mecha- nisms that generate the observed clusters. A similar contrast is visible in urban stud- ies. Satellite data and demographic statistics have been used to typologise post-socialist cities into trajectories of growth and decline (Sandu, A. 2024). While typologies describe variation, they risk reifying processes such as peri-urbanisation as homogeneous outcomes. By contrast, mechanism‐based analysis treats peri-urbanisation as the contingent product of property restitution, fragmented plan- ning, and capital inflows (Petrovici, N. and Poenaru, F. 2025). Here again, digital technol- ogies supply essential empirical material, but explanatory adequacy depends on situating them within stratified socio‐spatial contexts. This recursive relation between digital methods and mechanism-based reason- ing has broader implications for economic geography. First, it calls for methodological pluralism: quantitative models, qualitative evidence, and historical comparison must 293DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. be combined to identify the causal powers at work. Second, it highlights the importance of reflexivity about epistemic infrastructures: categories embedded in data collection and processing influence what becomes visible as a mechanism. Third, it shows the value of digital technologies is conditional: their explanatory power is realised only when used within theory-led research designs that account for institutional and spatial variation. Recent work supports this perspective. Dodgson, M. et al. (2014) show that innova- tion ecosystems require mechanism-based accounts that integrate digital data with insti- tutional analysis. Jessop, B. and Sum, N.‐L. (2022) stress that epistemic reflexivity is cen- tral to avoiding the reification of categories produced by digital infrastructures. Shelton, T. (2024) demonstrates that machine learning models in urban geography generate useful empirical insights only when interpreted through theories of governance and inequal- ity. Together, these contributions underscore that digital technologies are neither neutral instruments nor autonomous explanatory devices; they are epistemic infrastructures whose value depends on their integration into mechanism-oriented research designs. For post-socialist studies, this dual fram- ing is especially important. Imported digi- tal categories, such as “transition economies” or “emerging markets” can flatten regional ontologies and obscure hybrid institutional mechanisms (McElroy, E. and Chelcea, L. 2025). Yet when contextualised within local histories and comparative frameworks, digi- tal data can illuminate how socialist legacies interact with global pressures to produce novel causal configurations. In this way, Central and Eastern Europe is not merely a site of empirical testing but a region where digital infrastructures and mechanism-based reasoning together reveal processes of wider theoretical significance. Read in this way, digital spatial technolo- gies extend the empirical reach of geography but do not by themselves provide explana- tion. Their outputs should be read as traces of causal mechanisms situated in socio-technical infrastructures and filtered through specific analytic categories. Coupled with a critical realist ontology and a reflexive epistemol- ogy, these tools can help connect patterns to structures and events to generative powers. Without such embedding, they risk reinstall- ing a thin positivism through computation. For economic geography, and for Central and Eastern Europe in particular, the task is to use digital infrastructures as components of the- ory-led, mechanism-oriented designs rather than as self-standing explanatory devices. Empirical illustrations from Central and Eastern Europe The argument can be grounded in two short illustrations from Romania that work with the same families of variables but produce different kinds of explanation. The first con- cerns vaccine uptake and shows how spatial models identify robust patterns that require institutional specification to count as ex- planation. The second concerns peri-urban change and shows how typologies drawn from demographic and satellite data can be reinterpreted as traces of generative mecha- nisms that vary across metropolitan settings. In both cases the move from pattern to expla- nation depends on the dual view of context developed above and on the mid-range ori- entation in explanatory realism (Yeung, H.W. 2019, 2023; Paasi, A. 2025). Vaccine uptake in Romania Mare, C. et al. (2024) analyse county and lo- cal data on COVID‐19 vaccination together with socio-economic covariates. Spatial econometric specifications identify positive spatial autocorrelation and diffusion effects. These results show that vaccine uptake clus- ters and that neighbouring units co-vary in a systematic way. Poverty, settlement struc- ture and religious composition are correlated with the outcome and some effects propa- gate across administrative boundaries. Read Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299.294 at the level of the actual and the empirical, these findings support a model in which mechanisms are treated as regularities that may travel to similar settings subject to fur- ther testing. The account is predictive and precise, but the causal powers that produce the observed clusters remain unspecified. Petrovici, N. et al. (2023) re-embed the same empirical patterns in a stratified ontol- ogy of hybrid health regimes. The analysis reconstructs how socialist legacies of primary care and access, post-1990s market reforms, and transnational governance produced dis- tinct organisational arrangements for vacci- nation logistics, information and trust. In this reading the mechanisms are generative struc- tures. They include institutional layering in family medicine and public health, the organ- isation of professional authority and distrust, and the circulation of clinical and managerial guidelines across national and international bodies. Spatial clusters are treated as empiri- cal traces of these mechanisms rather than as explanations in themselves. The models remain useful because they indicate where the mechanisms are likely to be active and how their effects are distributed. Explanation requires stating the scope conditions under which particular combinations of mecha- nisms operate, for example the joint presence of fragmented primary care, targeted private provision and strong vertical guidance. This illustration clarifies the role of digital and statistical tools within mechanism-ori- ented research. Spatial econometrics shows where and how outcomes co-vary. It does not identify causal powers independently of theory and institutional evidence. The realist account provides that identification by linking traces to structures and by specifying condi- tions of activation. The result is consistent with a pragmatic explanatory realism that evaluates explanation by its capacity to uncover context- dependent mechanisms with stated scope rather than by predictive fit alone (Yeung, H.W. 2019, 2023). It also aligns with recent work on data infrastructures and epistemic effects, which cautions that model outputs codify assumptions about observables and therefore require reflexive interpretation (Ash, J. et al. 2018; Kitchin, R. 2022; Lynch, M. 2022). Peri-urban change in Romania Sandu, A. (2024) combines demographic in- dicators with satellite-derived measures of built-up area to classify post-socialist cities into trajectories of growth and decline. The typology is clear and comparable across many cases. If taken as sufficient for explanation, however, the mechanism behind peri-urban expansion during demographic decline is the correlation itself. The city appears as a bound- ed unit that moves across states defined by the data. The causal powers remain implicit. Petrovici, N. and Poenaru, F. (2025) work with the same kinds of variables but interpret them within a framework that treats peri- urban morphologies as spatial figurations. The analysis reconstructs how property resti- tution, fragmented planning, state and private capital in land and infrastructure markets, and the labour-housing nexus interact across met- ropolitan regions. In this reading the mecha- nisms are again generative and multi-scalar. Built‐up change and demographic decline are empirical traces of these mechanisms. The concept of spatial figuration specifies how par- ticular configurations of institutional and eco- nomic relations generate distinct peri-urban outcomes and it states when these mecha- nisms are likely to combine. The focus shifts from the typology of outcomes to the identifi- cation of causal powers and to the conditions under which they operate. As in the health case, digital sources are indispensable for identifying patterns at scale, but they require theoretical embedding to yield explanation. Remote sensing classifi- cations and demographic indicators supply the patterns. Mechanism‐based analysis sup- plies the link to structures and to scope condi- tions. This approach avoids treating Central and Eastern Europe as a set of anomalies and instead treats it as a region in which hybrid mechanisms are analytically visible and travel under specified conditions (Grubbauer, M. 295DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. and Kusiak, J. 2012; Paasi, A. 2025). It also responds to concerns about imported cat- egories in post-socialist research by showing how regional ontologies shape what counts as a mechanism and how categories must be inspected for fit before they are used for explanation (Müller, M. 2019; McElroy, E. and Chelcea, L. 2025; Nagy, E. 2025). Synthesis The two illustrations support three claims that follow directly from the theoretical ar- gument. First, the same data can sustain dif- ferent ontological commitments. If mecha- nisms are defined as regularities, explana- tion remains at the level of the actual and the empirical. If mechanisms are defined as generative structures, explanation requires institutional and historical specification and a statement of scope. Second, digital spatial technologies are best treated as epistemic in- frastructures that produce empirical traces to be linked to mechanisms. Their value for explanation rises when model outputs are read through theory-led designs and when categories embedded in data collection and processing are made explicit (Ash, J. et al. 2018; Kitchin, R. 2022; Lynch, M. 2022). Third, treating Central and Eastern Europe as an ontologically stratified region changes the research questions we ask and the cat- egories we use. Explanation depends on how socialist legacies, post-socialist reforms and transnational pressures interact to produce outcomes. This perspective avoids universal- ism and exceptionalism and supports com- parative work in which mechanisms travel only under clearly stated conditions (Yeung, H.W. 2019, 2023; Paasi, A. 2025). These illustrations therefore meet the empirical expectations that follow from the rest of the paper. They move from patterns to mechanisms with explicit scope conditions. They integrate digital methods without con- flating pattern with explanation. They show how a regional ontology shapes epistemic categories and, in turn, explanatory claims. Implications for mechanism-based research design The analysis above has two practical implica- tions for how we design studies in economic geography. First, explanation should proceed by specifying mechanisms and scope condi- tions before the choice of methods. Second, digital spatial technologies should be treated as epistemic infrastructures that yield empirical traces to be interpreted within a stratified ontol- ogy. In what follows we set out design princi- ples that follow from these claims and indicate how they relate to recent work in the field. A mechanism-oriented design begins with a clear statement of the causal powers that are hypothesised to operate, the socio-spatial conditions under which they are activated, and the empirical traces they are expected to leave. This framing translates the realist distinction between structures, events and observations into research practice (Bhaskar, R, 1975; Sayer, R.A. 1992). It is also consist- ent with explanatory realism, which evalu- ates theories by their ability to recover context-dependent mechanisms rather than by predictive fit alone (Yeung, H.W. 2019, 2023). In empirical terms this means formu- lating propositions that link a set of insti- tutional arrangements to a pattern that can be observed and then stating the conditions under which the link should hold. For exam- ple, a claim about related variety and branch- ing in regional development must identify the industrial and governance configurations through which that mechanism operates and the range of contexts in which it is expected to travel (Boschma, R.A. and Martin, R. 2010; Balland, P.-A. et al. 2019). Comparative strategy follows from this orientation. Cases should be selected to vary the conditions that are thought to enable or constrain a mechanism so that we can test its operation across settings. This can be done within a country, across countries within a region, or across regions where institutional architectures are comparable. The point is to avoid both abstract universalism and local exceptionalism by stating where the mecha- Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299. Benedek, J. and Ţoiu, A. Hungarian Geographical Bulletin 74 (2025) (3) 285–299.296 nism is likely to work and where it is not. In Central and Eastern Europe, for instance, the interaction between socialist legacies and market reforms can be treated as a structured source of variation rather than as a residual context, which allows mechanism-based claims to be examined across different insti- tutional mixes (Kinossian, N. 2022; McElroy, E. and Chelcea, L. 2025; Paasi, A. 2025). The use of digital spatial technologies should be aligned with these aims. Spatial econometrics, remote sensing and machine learning can identify clusters, discontinuities and co-variations at scale, but these outputs do not by themselves specify causal powers. Their role in a mechanism-oriented design is to locate and describe empirical traces and to help adjudicate between rival mechanism claims. This requires transparent reporting of model choices, variable construction and clas- sification schemes, alongside a discussion of the epistemic assumptions embedded in data infrastructures (Ash, J. et al. 2018; Kitchin, R. 2022; Lynch, M. 2022). It also requires com- bining quantitative outputs with historical and institutional evidence that bears directly on the proposed mechanisms. The goal is not method triangulation for its own sake but the use of diverse materials to identify and test the action of causal powers in stratified con- texts (Wyly, E. 2011). A further implication concerns categories. Because categories travel with data infrastructures and scholarly tra- ditions, researchers should document how key constructs are defined and whether they fit the regional ontology under study. This is particularly important when standard indi- cators and taxonomies originate in settings with different institutional architectures. Reflexive treatment of categories is part of the research design rather than an afterthought, since misfit can generate spurious regulari- ties or hide relevant mechanisms (Barnes, T.J. and Christophers, B. 2018; Jessop, B. and Sum, N.‐L. 2022; Paasi, A. 2025). In practical terms, this entails justifying the transfer of constructs, adjusting them where needed, and indicating how these decisions affect the identification of mechanisms. Evaluation criteria also follow from the foregoing. We propose four that can be applied to mechanism-based studies in economic geography. First, ontological clarity: are the mechanisms, structures and scope conditions explicitly stated and dis- tinguished from the empirical patterns they are meant to explain. Second, evidential fit: do the empirical traces produced by digital and non-digital methods correspond to the expected signs of the proposed mechanisms. Third, contextual specificity: are the institu- tional and spatial conditions under which the mechanism operates described in suffi- cient detail to allow comparison and limited generalisation. Fourth, epistemic reflexivity: are the categories and data infrastructures that structure observation made explicit and assessed for fit with the regional ontol- ogy (Ash, J. et al. 2018; Yeung, H.W. 2019; Kitchin, R. 2022; Paasi, A. 2025). These principles have consequences for field‐building. They encourage cumulative work in which mechanisms are carried across studies together with their scope conditions, rather than being replaced whenever new data become available. They favour designs that combine digital traces with institutional analysis and comparative evidence so that results can be interpreted as more than sur- face regularities. They also support the sta- tus of Central and Eastern Europe as a site for concept formation, since hybrid institu- tional arrangements in the region make certain mechanisms analytically visible and therefore useful for theory beyond the region when scope is stated clearly (Kinossian, N. 2022; McElroy, E. and Chelcea, L. 2025). Finally, the approach outlined here has lim- its that should be recognised. Mechanisms in open systems rarely operate in isolation, which makes identification and adjudication demanding. Digital infrastructures change rapidly and carry evolving epistemic effects that must be tracked. Not all mechanisms will leave traces that can be captured by current data. These constraints do not weaken the case for mechanism-based explanation. They indicate the need for careful design, transpar- 297DOI: 10.15201/hungeobull.74.3.4 Hungarian Geographical Bulletin 74 (2025) (3) 285–299. ent reporting and cumulative comparison so that claims about causal powers remain tied to the contexts in which they operate and the categories through which they are known. Conclusions This paper has argued that explanation in economic geography requires treating so- cio-spatial context as both an ontological condition and an epistemic infrastructure. Mechanisms operate in stratified settings shaped by institutions, politics and mate- rial arrangements (Bhaskar, R. 1975; Sayer, R.A. 1992). At the same time, the categories through which we recognise mechanisms are produced within data systems and scholarly traditions that travel unevenly across regions (Livingstone, D.N. 2013; Meusburger, P. et al. 2018; Lynch, M. 2022). When these two dimensions are addressed together, we can avoid the twin errors of abstract universalism and local exceptionalism. The paper devel- oped this claim in dialogue with explanatory realism. We adopted Yeung’s call to judge theories by their capacity to recover context- dependent mechanisms and to state scope conditions, and extended it by foreground- ing how practical ontologies shape the epis- temic categories of both social actors and researchers (Bhaskar, R. 1979; Yeung, H.W. 2019, 2023). We showed that digital spatial technologies are valuable when used to lo- cate empirical traces for theory-led inquiry but do not by themselves supply causal pow- ers (Wyly, E. 2011; Ash, J. et al. 2018; Kitchin, R. 2022). The two illustrations from Central and Eastern Europe made this point concrete. The same datasets can yield correlationist accounts or mechanism-based explanations depending on how they are embedded in in- stitutional histories and regional ontologies. The contribution is threefold. First, the paper clarifies how mechanism‐based expla- nation in geography depends on both onto- logical specification and epistemic reflex- ivity. Second, it offers design principles for mechanism-oriented research that link causal claims, scope conditions and empirical traces, and that align digital methods with comparative and historical evidence. Third, it reframes Central and Eastern Europe as a productive site for concept formation rather than a repository of anomalies, consistent with recent reconsiderations of post-social- ist studies (Grubbauer, M. and Kusiak, J. 2012; Kinossian, N. 2022; McElroy, E. and Chelcea, L. 2025; Paasi, A. 2025). The analysis points to a short research agenda. Future studies should code insti- tutional and governance features alongside standard quantitative indicators so that pro- posed mechanisms can be tested across clearly stated conditions (Boschma, R.A. and Martin, R. 2010; Balland, P.-A. et al. 2019). 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