“BREAKING DOWN BARRIERS”: DEVELOPMENT OF AN ENGINEERING TECHNOLOGY TO ENGINEERING TRANSFER PATHWAY IN CANADA
Bibliographic record
Abstract
College pathways significantly improve access to engineering degrees for marginalized or underrepresented students. Although several provinces in Canada have established pathways for students wishing to move from an engineering technology diploma to an engineering degree, such as Newfoundland and Labrador, Alberta, and British Columbia, no province-wide pathway in Ontario’s higher education system exists. As a result, transfer students are faced with a myriad of challenges and limited transfer pathways due to the institution-specific and complex nature of transfer articulation agreements in Ontario. This paper reports on the development of a province-wide diploma-to-degree engineering transfer pathway in Ontario. The proposed pathway program builds upon findings from a previous qualitative research study conducted by the co-authors which highlighted key factors necessary to develop future large-scale transfer pathways. The pathway was designed with the flexibility to incorporate extant institution specific pathways, while also providing a solid foundation for the development of a pilot multi-institutional bridge program. The challenges associated with creating a streamlined transfer pathway from engineering technology to engineering are myriad, and key outcomes from this project will continue to inform the development of possible approaches to a consistent, Ontario-wide engineering transfer program.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".