Les motifs évoqués par les enseignants débutants pour expliquer leur envie de quitter le métier et les implications pour soutenir leur persévérance
Bibliographic record
Abstract
Plusieurs recherches tentent actuellement de mieux comprendre les effets des programmes d'insertion professionnelle (PIP) implants pour remdier au dcrochage enseignant. N'ayant obtenu aucun rsultat significatif dans une tude quant aux diffrences entre les participants en fonction de leur participation un PIP, nous nous sommes intresss aux diffrences attribuables au fait d'avoir dj pens quitter l' enseignement. Cette tude vise comprendre pourquoi les enseignants dbutants ont eu cette pense et quelles en sont les implications pour les PIP. Les rponses de 59 participants deux questions issues de l' tude initiale ont t soumises une analyse de contenu. Les rsultats clairent leurs motifs qui sont trs diversifis et appuient la ncessit d'une combinaison de mesures dans les PIP.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".