FINDING THE GAPS: THE DEVELOPMENT OF A NETWORK FOR GRADUATE ATTRIBUTE PROFESSIONALS
Bibliographic record
Abstract
Abstract – To help fulfill the Canadian Engineering Accreditation Board’s new requirements, many institutions are creating positions focused on graduate attributes and the continual improvement process (GACIP). Due to the very recent development of this role, people hired as graduate attribute professionals (GAPs) have no established community in which to network and develop. In addition, the very nature of these positions is not well defined. This paper describes the development of the Graduate Attribute Professional Network, an informal community of people whose jobs are focused on GACIP, and the results of a survey conducted with its members. GAPs are found to generally be highly educated people, usually with an engineering background, many of whom have experience as educators. They tend to be new to their roles, to be spending 50% or less of full-time hours on GACIP-related duties, and to be involved in every aspect of graduate attribute assessment and the continual improvement process. GAPNet is an important resource to support these individuals who are so involved in engineering education and accreditation in Canada.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".