Prédiction des compétences émergentes par analyse textuelle
Bibliographic record
Abstract
Dans ce mémoire, l'objectif est de prédire les compétences émergentes en utilisant l'information contenue dans les offres d'emploi. Nous combinons le Least Absolute Shrinkage and Selection Operator (LASSO) et l'analyse de texte pour prédire les compétences émergentes. Pour y arriver, nous utilisons la description d'offres d'emploi dans le domaine des animateurs pour le cinéma et l'année de publication des offres. Les offres d'emploi utilisées s'étendent de 2014 à 2021 et proviennent d'un site spécialisé dans le domaine pour les régions de Québec et de Montréal. L'algorithme est en mesure de prédire les compétences émergentes et les compétences moins fréquemment demandées par les employeurs. Nous obtenons respectivement le logiciel Maya et le logicel Nuke comme compétence émergente et compétence de moins en moins demandée par les employeurs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".