APPROACHES TO GRADUATE ATTRIBUTES AND CONTINUAL IMPROVEMENT PROCESSES IN FACULTIES OF ENGINEERING ACROSS CANADA: A NARRATIVE REVIEW OF THE LITERATURE
Bibliographic record
Abstract
The CEAB accreditation requirement of graduate attributes and continual improvement processes (GACIP) has been a pervasive topic in the annual CEEA conference proceedings since 2010. The proceedings are a rich primary source of work being done in Canadian tertiary institutions. This narrative review of the literature consolidates and discusses the relevant CEEA papers for 2010-2017 in a manner that is useful to leadership and decision-makers at accredited faculties of Engineering nationwide. Four guiding research questions were asked of this literature: (1) What general frameworks are being implemented as accredited faculties of Engineering across Canada approach GACIP?; (2) What are the specific activities and methods of one or more of the GACIP steps?; (3) What are the roles and responsibilities of people involved?; and (4) What perspectives are taken in response to the CEAB accreditation criteria, including concerns, issues, and benefits? A qualitative content analysis was conducted on 106 papers meeting selection criteria. Emergent topics were used to form the discussion.
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.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".