Bibliographic record
Abstract
Résumé L'article porte sur la formation à l'employabilité. Après avoir clarifié le concept d'employabilité, décrit le contexte politique et les contenus de formation, et présenté l'évaluation qu'on a faite des programmes de formation à l'employabilité, les autrices défendent l'idée que, en favorisant un consensus autour de la pertinence de pratiques où chacun trouve son compte, le discours sur l'employabilité a eu pour effet de mobiliser un nombre important d'acteurs et ce, malgré le caractère inopérant de la plupart des interventions pour les sans-emploi. Les autrices ont choisi le concept habermassien de l'agir communicationnel pour discuter du pouvoir du langage sur les actions et repérer quelques enjeux liés à la formation à l'employabilité. The article focuses on employability. A clarification of the concept is followed by a description of the policies related to training for employability, their content, and some evaluative data. The authors defend the idea that, in favoring a consensus on the relevance of practices wherein everyone finds some profit, the discourse on employability has had the effect of mobilizing an important number of actors despite the ineffectiveness of most initiatives targeted at the unemployed. They ground their analysis on Habermas's theory of communicative action, discuss the influence of language on comprehension and enactment, and point to what is at stake in the area of employability.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".