Bibliographic record
Abstract
Collaboration between different business enterprises has become a must in our days of financial, technical and commercial complexity. It is highly encouraged by governments and businessmen. The traditional legal techniques known and frequently used in Canada appear however to be inadequate in some cases. The French legislator has innovated in the field by creating, in 1967, the legal framework of the Groupement d'intérêt économique (G.I.E.) The G.I.E. is an institution that has the separate legal entity of the corporation while maintaining the joint, several and illimited liability of the partners. The G.I.E. is all the way neutral. It is not aimed to generate direct profits for itself or the partners but allows the involved partners to have a better overall performance. Since 1967, over 9000 G.I.E. have been created in France to cover fields as different as the Airbus joint venture, communal maintenance services, research publicity or marketing department, buying or export offices, etc. This article, written by a leading academic, discusses the different legal aspect of the G.I.E. and explains the pros and cons of the institution.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".