Bibliographic record
Abstract
Les microgroupes sont considérés comme un phénomène en expansion mais encore mal connu dans son ampleur et ses origines. Le but de cet article est d’explorer ce nouveau mode d’organisation des PME dans cette double dimension. La première partie, de nature factuelle, procède à une évaluation quantitative de cette forme d’organisation et de sa contribution aux grands indicateurs macroéconomiques français. La seconde cherche à comprendre les raisons d’être des microgroupes et explore à cette fin la littérature en sciences de gestion et en économie, ce qui conduit à mettre en évidence un clivage entre les choix individuels de l’entrepreneur et les contraintes organisationnelles liées notamment à la sous-traitance. Nous concluons en soulignant que la prise en compte du critère d’indépendance et des microgroupements d’entreprises peut conduire à revisiter la séparation entre PME et grandes entreprises.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".