Implanter le rangement forcé des employés selon leur rendement : prudence !
Bibliographic record
Abstract
Résumé L’implantation de la méthode du rangement forcé au sein de quelques grandes organisations (par exemple, General Electric) ainsi que des cas d’échec ou d’abandon dans d’autres organisations (par exemple, Ford Motor) ont suscité un intense débat sur les mérites de cette pratique. Cet article vise à aider les dirigeants et les cadres à mieux comprendre l’ambiguïté qui persiste sur le recours à cette méthode. Pour y parvenir, nous définissons ce qu’est le rangement forcé et ses objectifs. Ensuite, nous traitons de ses limites, de ses atouts et de ses préalables. Finalement, nous analysons des méthodes alternatives susceptibles d’être privilégiées étant donné qu’elles peuvent s’avérer moins risquées, plus productives et plus respectueuses des personnes et des lois. Fonctions : GRH, management
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".