Profils coévolutifs au sein de groupements hétérogènes : cas de PME exportatrices malgaches
Bibliographic record
Abstract
Pour s’internationaliser, les PME s’impliquent parfois durablement dans des groupements intégrant des acteurs directs locaux (producteurs et/ou transformateurs) et des firmes étrangères chargées de les aider à accéder aux marchés étrangers. Pour identifier et analyser les profils qui contribuent au processus de coévolution des membres de ces groupements hétérogènes, cette recherche examine les conditions qui permettent de créer dans le temps un avantage commun tout en permettant de servir un projet individuel. Deux cas complémentaires de groupements associant des PME exportatrices malgaches et des firmes étrangères sont étudiés, de manière longitudinale et qualitative, pour comprendre un tel processus. Fondée sur une approche abductive, la mise en relation de ces cas permet d’identifier deux profils séquentiels d’acteurs qui conditionnent le potentiel coévolutif des parties impliquées : les profils comportementaux (capacités réactives permettant de faire face aux conditions de l’environnement externe) et les profils managériaux (attitudes proactives contribuant à impacter l’environnement externe).
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".