Does Transfer Pathway Uptake Help or Hinder Access to <scp>STEM</scp> Fields in Postsecondary Education? A View From Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it