INVESTIGATING THE RELATIVE IMPORTANCE OF THE CEAB GRADUATE ATTRIBUTES: STUDY DESIGN AND INITIAL FINDINGS
Bibliographic record
Abstract
Abstract - This study represents the PhD thesis research of the lead author. The greater study is designed as a mixed-methods explanatory case study. The overarching objective is to explore whether the CEAB graduate attributes are emphasized in the engineering programs in the Faculty of Engineering at the University of Manitoba in the most optimal way. The first phase of the study is designed to determine the relative importance and the levels of dependencies of the graduate attributes across three engineering stakeholder groups: faculty, students, and industry members. In this paper, the design and methodology of the first phase of this mixed methods research study are explained, and the preliminary findings from the participation rates and participants’ familiarity with the graduate attributes on the survey data are presented. Results show that a sizable percentage of students, faculty and industry members are not highly familiar with the CEAB graduate attributes. Therefore, work to develop a common knowledge about the CEAB graduate attributes needs to continue.
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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 | 0.035 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".