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Record W4404044143 · doi:10.1002/rev3.70007

Toward a developmental transactional model of educational upward mobility

2024· article· en· W4404044143 on OpenAlexafffund
Y. Lee, Michael J. MacKenzie, Lucyna Lach

Bibliographic record

VenueReview of Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec-Société et CultureWilliam T. Grant Foundation
KeywordsTransactional leadershipPsychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract There is a disconnect between theoretical depictions of educational upward mobility (EUM) and empirical evidence. Although the Origin‐Education‐Destination (OED) triangle, a functionalist model in its ideal meritocratic state, posits education as the key mediator between one's origin and destination, efforts to address inequality through higher education have not always proven successful across regions and generations. This paper proposes an adapted theoretical model of EUM, drawing on critical theoretical analysis from multidisciplinary perspectives—including the functionalist perspective of education, Bordieuan critiques of social reproduction, and the ecological‐transactional theory of human development—to capture the interplay of developmental processes and structural inequalities. Departing from the original linear mediation approach of OED, the new model attempts to account for research findings of how origin factors moderate the E‐D association, and how educational institutions and programmes can moderate the O‐D relationship by leveraging social reproduction theory and the ecological‐transactional framework. The transactional developmental lens adopted by this article illuminates EUM as a dynamic, fluid process of human development involving ongoing, dialectic transactions between individual agency and the ecological context across life courses. A critical review of existing psychological and sociological theories in the domain of EUM highlights the need for more fine‐grained longitudinal data and diverse approaches to change the status quo by considering individual differences, micro‐relational dynamics and macro structures across the ecology and across development. The article also acknowledges, however, the methodological, theoretical and contextual limitations of the integrated model, calling for future studies to account for cultural, societal and generational variations to gain a more comprehensive understanding of the underlying processes in order to guide future policy and programmatic directions. Context and Implications Rationale for this study: Education is often held up as the great equaliser, yet despite efforts over decades by governments to prioritise higher education as a tool to break the intergenerational inequality, we have seen limited success in educational opportunity driving reduction of the linkages between origins and outcomes. Why the new findings matter: To effectively push forward our understanding of the systems change and policy interventions necessary to meaningfully move the needle in educational upward mobility will require a conceptual model that can effectively contend with the dynamic developmental nature of these processes in context. Implications for researchers and policy makers: This critical review reveals the complicated, fluid nature of the role of education in upward mobility. Changing the inertia, or event backslide in upward mobility, will necessitate scholars and policy makers amplifying their collective agency by considering the dynamic transactions of individual differences, micro‐relational dynamics, and macro‐structures in their academic, practical and policy efforts toward addressing inequality. Through pushing our thinking on upward mobility toward a more developmentally informed model, our science will more fully elucidate the underlying complexity across levels of the ecology that either facilitate, or serve as barriers to, increasing opportunity and broadening the possibilities for all youth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.006
Scholarly communication0.0040.006
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.127
GPT teacher head0.426
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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