Creative Attitude : Pour inspirer, motiver, collaborer et innover en entreprise
Bibliographic record
Abstract
« Creative Attitude comble une lacune majeure de la reflexion et de la litterature manageriale. Il le fait d’une maniere originale, creative et concrete. Je suis convaincu qu’il n’y a pas de responsable d’entreprise, grande ou moyenne, qui n’y trouve matiere a reflexion, a action et a progres. »Louis SCHWEITZER,President d’honneur de Renault« Nous sommes entres dans l’âge des ruptures permanentes. Peu de previsions se verifient. C’est pourquoi nous avons besoin de creativite et de dirigeants, leaders et managers, qui excellent en matiere d’innovation. Dans un monde empreint d’echec, Creative Attitude les guidera vers la reussite. A lire absolument. »Nancy J. ADLER,Professeur de management a l’Universite McGill de Montreal,Auteur de Leadership InsightAdoptez la Creative Attitude ! Pour exprimer votre singularite, favoriser le travail collaboratif, manager et diriger en toute confiance, laissez-vous guider par cet ouvrage pratique et inspirant.Tres accessible et concret, ce livre vous propose :– plus de 50 temoignages de leaders, managers, artistes et experts ;– 7 cas d’entreprises et 8 exercices pour vous entrainer a la Creative Attitude ;– 25 creations graphiques realisees par des artistes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".