Does Transfer Pathway Uptake Help or Hinder Access to <scp>STEM</scp> Fields in Postsecondary Education? A View From Canada
Bibliographic record
Abstract
ABSTRACT Considerable scholarly attention has been devoted to how gender, race and various other demographic factors shape the odds of majoring in science, technology, engineering and mathematics (STEM) programs. Such work has identified sizable disparities in access to STEM fields across various dimensions. In turn, these empirical findings have informed productive discussions about the social and institutional mechanisms that prevent marginalised groups from entering STEM, along with the potential strategies that could be used at multiple levels (e.g., government and institutional) to address them. Despite the increasing size of this literature, little energy has been devoted to examining the extent to which uptake of transfer pathways is associated with the odds of eventually majoring in a STEM field. Does transfer divert students away from STEM fields? Does it primarily function as an ‘on‐ramp’ for students from other disciplines to enter STEM? We find that students who travel transfer pathways into the university sector are less likely to major in STEM, but those that travel transfer pathways into the community college sector are more likely to major in STEM. We identify some of the mechanisms that could be contributing to these trends and highlight some prospective strategies for addressing the potential structural barriers faced by students wishing to enter STEM.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".