Bibliographic record
Abstract
Résumé L'auto‐coordination en entreprise, ou coordination des activités produite par les employés eux‐mêmes plutôt que par des tiers, est un phénomène encore mal appréhendé par les sciences administratives. La notion d'ajustement mutuel avancée par certains auteurs apparaît notamment comme une notion passablement réductrice. Cet article fait part de certains des résultats d'une recherche exploratoire effectuée récemment dans une entreprise québécoise de services sur le thème de la coordination, qui confirment les limites des théories courantes en matiére d'auto‐coordination, et qui apportent diverses pistes d'élargissement de la compréhension de cet espace de pratiques. Abstract Self‐coordination within an enterprise, or the coordination of activities performed by the employees themselves rather than by third parties, is a phenomenon that remains poorly understood by the administrative sciences. In particular, the notion of mutual adjustment put forward by some authors appears to be a rather limited one. This article presents some of the results of an exploratory research work conducted recently in a Quebec services enterprise on the topic of coordination, confirming the limits of current theories on the subject of self‐coordination, and opening up various avenues for broadening our understanding of this area of practices.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".