Bibliographic record
Abstract
Je m'appuie sur l'économie politique féministe pour argumenter qu'il nous faut changer notre approche. Au lieu de nous concentrer sur les structures du travail axées sur la déqualification et le contrôle de la maind'œuvre ou sur les individus et leur apprentissage formel, nous devons nous interroger sur les conditions qui empêchent les individus d'acquérir et d'utiliser les compétences voulues et réfléchir aux différents moyens de tenir compte du facteur temps dans la façon d'évaluer les compétences. Notre article se veut d'abord une intervention théorique dans le débat sur les compétences, mais qui prend racine dans une préoccupation très concrète : les compétences requises dans le domaine des soins de santé. Using a feminist political economy lens, I argue that there is a need to change how we approach skills in political economy. Instead of focusing solely on labor processes that deskill and limit control (as much of the rich political economy literature does, in this journal and elsewhere), or on individualized formal learning (as much of the management literature does), we need to ask what prevents people from developing and using the skills they need for their work, and how time can be factored into skill assessment. The argument is theoretical, but grows out of a practical concern with skills in health care.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".