To Be or Not to Be a Profession: Pros, Cons and Challenges for Evaluation
Bibliographic record
Abstract
Debates on the professionalization of evaluation regularly fuel controversies. Evaluation literature contains varied points of view in favour of or against means of restricting access to the profession and quality control mechanisms. This article examines the aims pursued (e.g. institutionalization, quality improvement, ethical practice) and challenges faced by the promoters of the professionalization of evaluation. It also presents the mechanisms and means envisioned in Canada by the Société québécoise d’évaluation de programme (Québec Society of Programme Evaluation: SQEP) designed to address these points. These mechanisms include the drafting process of an evaluation charter, membership of a professional order and evaluator certification. This article is based on a documentary review and an analysis of semi-directed interviews conducted with current and former members of the SQEP and its administrative council. These results help to fuel debates in matters of the professionalization of evaluative practice which arise in most contexts where evaluation has reached a certain maturity.
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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 | 0.450 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.013 | 0.101 |
| Scholarly communication | 0.042 | 0.031 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".