Bibliographic record
Abstract
Résumé La perspective structurationniste permet de mettre la stratégie d'entreprise sous le microscope afin de regarder comment elle se forme au quotidien. À travers différents jeux de langage, la stratégie est mise en acte dans les multiples interactions que les gens ont entre eux dans le cours de la vie ordinaire de l'entreprise afin de modifier les liens qu'ils entretiennent avec leur environnement. À partir d'une démarche d'ethnographie organisationnelle, sont reconstruits deux épisodes illustrant la transformation des systèmes stratégiques lors d'un changement d'orientation dans une entreprise de vêtements haut de gamme. Ces épisodes démontrent comment les acteurs utilisent les règles et les ressources de l'entreprise pour structurer de nouveaux liens avec la clientèle. Surcodage et traduction sont deux microdynamiques inhérentes à la compétence stratégique des acteurs. Abstract In this paper, a structurationist perspective is used to understand strategy making as an everyday practice. Through their ongoing activities, interactions and language games, people come to modify linkages with the organization's environment. An organizational ethnography approach is used to reconstruct two episodes that illustrate the transformation of strategic systems during a change in orientation of a top‐of‐the‐line clothing company. These episodes demonstrate how people use the company's rules and resources to structure new links with their clientele. “Over‐coding” and “translating” are two micro‐dynamics that are inherent to actors' strategic competence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".