Les mécanismes du don/contre-don : un chaînon manquant entre la GRH et l’innovation en PME
Bibliographic record
Abstract
S’inscrivant dans une perspective relationnelle, cet article vise à comprendre les mécanismes du don/contre-don constituant un chaînon manquant entre la GRH et l’innovation en PME. Au travers de la théorie du don/contre-don, le cas d’une PME française et plus précisément un projet d’innovation technologique développé de 2013 à 2016 a été étudié. L’analyse permet d’aboutir à une structuration des données et à une modélisation selon la méthode dite « à la Gioia ». Les résultats révèlent que la logique du don évolue au travers de deux étapes clés : libérer des dons visant à innover, et refuser de rendre des dons en matière de GRH. Ces étapes soulignent l’enjeu de la présence d’un contexte organisationnel propice, de liens sociaux intenses et du rôle de certaines pratiques de GRH.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".