L’embauche en ligne dans le secteur de la haute technologie est-elle meilleure?
Bibliographic record
Abstract
En mars 2021, Learning Management Pro (LMP) préparait la réouverture de ses bureaux après deux ans de pandémie. L’ensemble du personnel travaillait à distance, et l’équipe des ressources humaines menait en ligne tous les entretiens d’embauche ainsi que l’intégration des recrues. Le virage vers le recrutement et l’intégration en ligne présentait des avantages comme des inconvénients, et Asha Jemerson, à la tête du service des ressources humaines de LMP, se demandait si l’entreprise gagnerait à reprendre ses méthodes traditionnelles prépandémiques. Ce cas, inspiré de vraies personnes et organisations, est conçu à des fins de formation en ressources humaines à tous les cycles universitaires; il explore des concepts liés à la transition de la réalisation de tâches traditionnellement effectuées en personne vers un environnement virtuel.
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 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".