Mesurer l’intention de rester ou l’intention de quitter… telle est la question !
Bibliographic record
Abstract
Cette étude évalue deux manières de formuler la mesure d’intention de départ (c.-à-d. de rester ou de quitter) chez des membres des Forces Armées Canadiennes (FAC). Deux objectifs sont poursuivis : (1) déterminer la variance partagée par ces deux mesures et (2) comparer la force des corrélations qu’entretiennent chacune des formulations avec des indices de satisfaction (c.-à-d. au travail et dans la vie). Cette étude exploite 5069 questionnaires recueillis au sein des FAC. Les résultats montrent que les deux formulations de la mesure d’intention ne partagent qu’une faible part de variance. On y observe également que les différentes formulations de la mesure d’intention entretiennent des corrélations semblables avec les deux indices de satisfaction. Malgré le caractère exploratoire de cette étude, les résultats observés invitent praticiens et chercheurs à la réflexion ainsi qu’à la nuance le moment venu de mesurer l’intention à maintenir (ou non) le lien d’emploi.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".