Towards a scale and tool for the appraisal of CEAB attributes - Progress report on a field test
Bibliographic record
Abstract
CEAB 2014 requires the appraisal of ‘attributes’. Quasi-competencies are not easy to ‘measure’. Results risk having a ‘local’ meaning with little transferability between institutions. From its interest into CDIO, and in parallel to its partaking in the DOCET project, École Polytechnique developed a 7-level scale, fieldtested by nearly 100 appraisals pre-graduation work experiences. The paper summarizes the rationale behind the 7-level scale when compared to the 5-level CDIO scale, the possible mapping onto the EQF, and reports on the appraisals of students after 16 weeks internships. Supervisors appear to use the tool as a relative Likert-type scale, and will have to undergo a learning curve. Attributes appraisal tools need to be engineered rather than to be determined by consensus.
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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 | 0.113 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".