Le cas EXFO : la valorisation du capital humain dans un contexte interculturel et intergénérationnel
Bibliographic record
Abstract
Résumé Que l’on adopte le point de vue de l’innovation, celui de l’amélioration de la compétitivité des organisations ou celui de la gestion des connaissances, on observe que la valorisation du capital humain constitue un défi important en ce qui concerne le soutien à la croissance des organisations oeuvrant sur des marchés très concurrentiels. Le but de ce processus de valorisation consiste à transformer les savoirs explicites et tacites individuels en savoirs collectifs innovants. Cette étude de cas vise à présenter la stratégie des ressources humaines d’EXFO, une organisation qui évolue dans un contexte de haute technicité et de concurrence féroce, celui de l’optique-photonique. La discussion de ces pratiques est précédée d’une présentation de l’entreprise, des caractéristiques de sa main-d’œuvre et des défis qu’elle doit affronter quant aux ressources humaines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".