L’évaluateur et la sagesse pratique : vecteurs essentiels pour assurer la crédibilité de l’évaluation
Bibliographic record
Abstract
Abstract: This article revisits data collected during previous research conducted by Hurteau and Houle (2008). At the time, the researchers were not able to account for the evaluator’s personal skills to pilot an evaluation, the results of which were acceptable to the stakeholders, and to conduct a comprehensive analysis, for lack of an appropriate theoretical framework. By introducing the concept of practical wisdom, House (2015) offers them the opportunity to do so. The author defines this concept as “… doing the right thing in the special circumstances of performing the job” (p. 88). In addition, Schwartz and Sharpe (2010) offer criteria used to establish its presence. The current approach consisted of analyzing the testimony of an experienced police officer. The results helped to improve our understanding of the concept, highlighting the contribution of Telos (moral values) as well as additional criteria such as the importance of time for reflection and the need to supplement the information where necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.130 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".