Les technologies de l’information et de la communication (TIC) et la fonction Ressources Humaines (RH)
Bibliographic record
Abstract
Analyse tantot comme une veritable revolution, tantot comme un simple « feu de paille », le developpement des TIC a eu plusieurs types de consequences au sein des entreprises. Ces technologies n’ont pas seulement modifie le contenu du travail. Elles ont egalement ete a l’origine de transformations organisationnelles. Ainsi, la question de l’impact des TIC sur la fonction Ressources Humaines (RH) ne se resume pas aux mutations des pratiques de GRH resultant de l’informatisation des systemes. L’integration des TIC interpelle aussi la place et l’organisation de la fonction RH au sein de l’entreprise. Ce sont ces deux aspects que Michel Kalika et Florence Laval analysent dans cet article, avant d’envisager la question sous l’angle des professionnels de la GRH, places dans une situation ambivalente : simultanement acteurs et objets des transformations liees a l’introduction des TIC dans les entreprises, leurs reactions sont contrastees.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".