Gérer les idées pour mieux innover
Bibliographic record
Abstract
Les gestionnaires sont de plus en plus convaincus que la génération et le développement d’idées représentent des enjeux prioritaires pour l’organisation. Gérer stratégiquement les idées permet à leur organisation d’innover plus souvent, plus rapidement ou plus radicalement. Cela peut contribuer, d’une part, à maintenir des parts de marché ou à en gagner de nouvelles et, d’autre part, à diminuer les coûts d’investissement, de maintenance et d’exploitation. Cependant, les modalités de la gestion des idées demeurent floues. Se basant sur l’étude d’une initiative de gestion des idées menée à l’Institut de recherche d’Hydro-Québec et sur la littérature dans ce domaine, cet article présente les paradoxes et les défis de la gestion des idées, puis décrit quatre propositions pour relever ces défis : commencer par les questions courantes ; accepter l’existence d’un réservoir tacite, le stimuler et savoir l’utiliser ; assurer la cohérence des systèmes ; transformer les ateliers de partage en occasions de briller.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".