Good, bad, or somewhere in between? A content analysis of perceptions of CEAB accreditation through CEEA proceedings
Bibliographic record
Abstract
This paper investigates the perceptions of interested and affected parties regarding the Canadian Engineering Accreditation Board (CEAB) accreditation system, which is of importance amidst Engineers Canada’s ongoing initiatives such as the Accountability in Accreditation (AinA) and the Futures of Engineering Accreditation (FEA) projects. Through a content analysis of adjectives and adverbs in the proceedings of the Canadian Engineering Education Association (CEEA) conferences from 2020 to 2022, this study reveals a predominantly neutral impression of the CEAB accreditation system. Qualitative analysis highlights concerns such as feasibility and effectiveness alongside recognition of accreditation's benefits in ensuring program quality and student success. Thematic codes shed light on various aspects of engagement with the CEAB accreditation system. The findings may contribute to informed decision-making processes and discussions within the engineering education community around improvements and the future of the CEAB accreditation system.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".