Pathways to Post-Secondary Education: A Multi-Method Investigation of the Impacts of Support Systems and School Belonging on Students’ Post-Secondary Intentions
Bibliographic record
Abstract
This dissertation explores the factors influencing students’ transitions into post-secondary education (PSE) in Canada, focusing on the roles of family, school, and community support systems. The first chapter provides a systematic review of the literature on student transitions, identifying key influences such as family dynamics, geography, and race. The second chapter examines how students' sense of belonging – shaped by parental support, school environment, and teacher relationships – affects their PSE intentions, with a focus on the mediation relationship of race and gender and the moderating relationship of streaming and race. The third chapter presents a case study of an after-school STEM program engaging at-risk and underrepresented youth in Hamilton, Ontario, highlighting the program’s role in fostering academic and personal growth, positive PSE attitudes, and sense of belonging. Through this exploration, the dissertation emphasizes the importance of supportive structures both in and outside of school in facilitating successful transitions to PSE. The findings contribute to understanding how educational systems can better support at-risk students in achieving their post-secondary goals.
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 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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".