“MEET THEM WHERE THEY’RE AT”: GATHERING INSTITUTIONAL PERSPECTIVES ON ENGINEERING TECHNOLOGY TO ENGINEERING TRANSFER IN CANADA
Bibliographic record
Abstract
This paper presents an investigation to determine the state of Canadian engineering transfer pathways and programs, how they were developed, and how to develop future large-scale transfer pathways. Technology to engineering pathways disproportionately improve access to engineering degrees for visible minorities, with some students relying on transfer as a pathway to a baccalaureate degree. However, there is no province-wide pathway in Ontario’s higher education system, so efficient transfer to engineering happens in a very a limited number of programs. To understand the system, a qualitative research study was developed that used semi-structured interviews with 15 institutions or groups with existing or attempted engineering transfer pathways. Results indicate that there are four factors differentiating existing pathways: timeline, structure, development, and scale. New partnerships should consider communication, collaboration, consideration of students and other institutions, and accreditation concerns as paramount in the success of proposed pathways, while lack of sustained institutional commitment, maintenance of programs, knowledge dissemination, and capacity may present challenges.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.049 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".