Bibliographic record
Abstract
Les competences requises d’un evaluateur sont sans aucun douteun sujet qui s’echauffe dans plusieurs domaines. Ce numero specialarrive donc a point et fournit la premiere analyse comparative desavancees internationales en matiere de formulation de competencespour les evaluateurs. Les riches descriptions des arguments propresau contexte pour la formulation de competences et les processus pourleur developpement sont en elles-memes eclairantes. Elles permettentde mieux comprendre les approches et produits emergents, tous fortdifferents les uns des autres. De superbes textes d’analyses viennentencadrer des lectures descriptives de cas. Ces dernieres presentent ala fois le cadre historique et theorique necessaire a la comprehensionde ce qui se passe et ne se passe pas dans le domaine des competencesen evaluation, de l’accreditation, et de la certification. Il est a noter quece numero presente aussi des points de vue critiques qui suggerentque les efforts fournis a ce jour ne sont pas assez bases sur des theoriesdu changement et n’adressent pas assez les competences moinsmesurables comme la disposition et les attitudes des evaluateurs.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.020 |
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".