Identifying Common Themes in Technical Graduate Attribute Indicators Across Canadian Universities
Bibliographic record
Abstract
The CEAB requires all accredited Canadian engineering programs to ensure that their graduates acquire competency in specific areas at the time of graduation. By design, every institution is expected to define their own measurable indicators for every graduate attribute and to track these indicators across its programs. This leads to a wide variation of indicators between universities. The goal of this paper is to provide a review of the technical graduate attribute indicators of a selection of accredited Canadian engineering programs and to identify common themes among them. The research question for this review paper is: What are the common themes among the technical graduate attribute indicators from accredited Canadian engineering programs? This graduate attribute indicator review can be used as a resource by universities who are engaging in the indicator redesign process.
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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 | 0.043 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.031 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".