Rethinking Stratification in Post-Secondary Education: Organizationally Maintained Inequality
Bibliographic record
Abstract
Decades ago, Boudon theorized that highly differentiated education systems would generate higher degrees of inequality than would more homogenous counterparts. In highly differentiated systems, at each point where students were afforded the freedom to select among institutions, Boudon believed that family-based knowledge and capital would produce stratified choices. The presence of differentiation would ensure that students from different SES backgrounds would be distributed in a non-random fashion across education systems. Contemporary theories of stratification within the sociology of education have astutely examined the role played by differentiation. Theories of effectively maintained inequality (EMI) and maximally maintained inequality (MMI) have led the way in this respect. MMI, for example, has theorized the role played by credential levels, depicting privileged students as migrating towards progressively higher credential tiers, and the organizations that service them. The EMI tradition, on the other hand, points to differences in organizational prestige, noting that privileged students migrate to the elite schools within any credential tier. Both theories highlight important dimensions of the hierarchical structure of education and how they can inform our understanding of how individual level stratification occurs through them. That being said, these theories focus only on two basic dimensions of organizational differentiation. They also tend to focus only on differentiation as it occurs within the university sector. In this dissertation I bring stratification research into conversation with organizational theory in the hopes of developing a more sophisticated and holistic understanding of differentiation, and thus, social stratification through PSE. I draw on insights from organizational theories to argue that, beyond credential tiers and prestige, PSE organizations are differentiated by the type of relationships (‘stratified connections’) they share with their surrounding environments, organizational networks and organizational sagas. I demonstrate that these often ignored mechanisms actively magnify organizational inequalities that exist within and across sectors of PSE. I adopt a mixed methodological approach for this dissertation. To examine organizational relationships with external environments, I draw upon sources documenting the characteristics of economic regions, as well as institutional data on program offerings and organizational structures. To examine organizational networks, I use a qualitative comparison of affiliation data, association documents and interviews. Lastly, to examine disparities in organizational sagas and symbols, I examine promotional materials available on institutional websites as well as other official documents. Such a versatile approach is needed given both the diverse group of questions I explore and the scope of my analysis. Addressing all sectors of Ontario PSE forces me to creatively overcome numerous data deficiencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".