Three Studies on Patterns of Educational Stratification Over Time, Across, and Within Cohorts in Canada and the United States
Bibliographic record
Abstract
Education is central to the study of social inequality. Governments and humanitarian organizations across the world believe in the right for all children to have equal access to education and to benefit from it regardless of the social and economic conditions in which they are raised. For this reason, the study of inequality in education is important to understanding education’s role in processes of social stratification. However, population levels of educational attainment are increasing, and the way individuals experience education is changing. For example, high school completion has become near universal, participating in higher education is more common, and processes of educational attainment are happening later in life alongside family and work. What do these changes mean for patterns of educational attainment and stratification?\nDrawing on nationally representative cross-sectional and longitudinal survey data from Canada and the United States, this dissertation conducts three empirical studies that examine patterns of inter- and intracohort change in educational stratification. Specifically, Chapter two asks whether and how the intergenerational transmission of education in Canada has changed between 1969 and 2016. It finds that increases in educational attainment over this time have not translated into more equal access to education for Canadian youth. Chapter three asks how adult participation in formal postsecondary education in the United States has changed across race, gender, and birth cohorts between 1978 and 2019. It shows that declines in adults’ overall rates of postsecondary participation over time masks important changes in the type and intensity in which adults in the United States are pursuing postsecondary schooling. Chapter four examines work-to-school transitions. It asks whether employment trajectories are related to the probability and timing of educational transitions across early adulthood within a cohort of American adults. It shows that more than 25 percent of adults pursue formal schooling between the ages of 28 and 37.\nTogether, these studies extend and expand previous research on demographic trends in educational stratification over time, across, and within cohorts in Canada and the United States. In so doing, this dissertation helps to understand how educational stratification is changing and what that means for education’s role in social inequality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".