Bibliographic record
Abstract
In the Canadian province of Ontario, higher education institutions have amplified their efforts to advance social equity and inclusion by establishing transfer programmes between colleges and universities. However, transitioning between these institutions continues to present challenges for the policy objectives assumed in transfer programmes. Few studies have analysed how students from historically marginalised backgrounds experience the transfer process, and how these experiences present a challenge for the ability of transfer pathways to function as a mechanism of equity and inclusion. Our study sheds light on the experiences of an important section of this population: Black college-to-university transfer students. Underpinned by the theory of intersectionality, our study critically explores the challenges that Black transfer students encounter in their transferring and settling into Canadian universities. Utilising in-depth interviews, our exploratory qualitative analysis shows that Black transfer students face a host of challenges linked to their race, class, and gender. These experiences impact students’ ability to transfer smoothly into their new school and pursue their academic goals in a timely fashion. Major issues include, but not limited to, the racism of low expectations, lack of representation within the transfer ecosystem, lack of support that considers the diversity within Black transfer students, and information asymmetry. Although we focus on the narratives of Black transfer students in the Canadian academy, this research advances the cause of equity by helping the higher education communities worldwide to reflect on how educational pathways can help higher education become a meaningful corrective of social disadvantage.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".