COGAF: A MANAGEMENT FRAMEWORK FOR GRADUATE ATTRIBUTES ASSESSMENT
Bibliographic record
Abstract
The objective of this paper is to introduce COGAF (a Common Graduate Attributes management Framework), an end-to-end framework that can be used by Canadian higher-level education institutions for integrating and managing the assessment of CEAB (Canadian Engineering Accreditation Board) graduate attributes in a systematic manner. COGAF is designed around four components: Governance, People, Process, and Technology (GPPT). The governance component consists of a set of artifacts to guide the execution of a graduate attributes assessment project. It addresses the ‘what should be done and why’ questions. The PPT components address the ‘how’ and ‘when’. The people component focuses on setting the right conditions to select, train, motivate, and retain the people who will operate COGAF. The process component focuses on the activities that need be carried out during the assessment project, whereas the technology component looks at tools (e.g., software applications) and technological platforms to support smooth execution of the assessment project.COGAF relies on strong management practices. It is meant to be a turn-key solution to be used by any institution that wishes to engage in integrating graduate attributes in their programs. COGAF is easily customizable to fit the needs of small and large institutions.
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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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".