Trusting organizations : Les organisations créatrices de confiance
Bibliographic record
Abstract
Pour les entreprises, la confiance devient le carburant vital de la performance. La conjonction d’une mondialisation de la concurrence de plus en plus imprévisible qui impose des réactions rapides, de la révolution numérique qui périme à toute allure des secteurs d’activité, métiers et emplois, des nouveaux modes de production de biens et de services qui multiplient les communications horizontales aux dépens des seules relations top-down, tout cela suppose que l’entreprise devienne sans cesse plus agile, s’adapte en temps réel, innove en permanence et passe d’un fonctionnement en silos à un fonctionnement transversal : seule une confiance fortifiée entre ses diverses parties prenantes peut assurer, au plus faible coût, cette indispensable et nouvelle conception de la performance. Partant de nombreux exemples d’entreprises canadiennes et européennes, les deux auteurs – eux-mêmes issus du monde de l’entreprise – proposent des outils simples pour permettre, d’une part, d’élever le niveau de confiance interpersonnelle entre tous ceux qui participent en interne à la production de la performance et, d’autre part, de rendre l’organisation même de l’entreprise créatrice de confiance vis-à-vis de l’ensemble de ses partenaires. C’est concret, pratique et nécessaire.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".