Benoit Duguay, 2015, Consommer, consumer. Dérives de la consommation , Québec, Ed. Liber, 158 p.
Bibliographic record
Abstract
Le transfert de connaissances dans le cadre d’une joint-venture demeure encore un domaine peu connu. À travers cette étude on propose une vision originale en précisant la nature et la dynamique d’une relation triale (entreprises mères et joint-venture) et interactive du transfert de connaissances. Les résultats montrent, tout d’abord, que la réussite de transfert dépend de la qualité des contributions de l’entreprise mère étrangère, de l’entreprise mère locale et de la joint-venture. Ensuite, nous avons pu identifier des facteurs déterminants et des facteurs facilitateurs du transfert de connaissances de l’entreprise mère étrangère vers la joint-venture, de l’entreprise mère locale vers la joint-venture et de la joint-venture vers les entreprises mères. Autant les entreprises mères que la joint-venture doivent les prendre en compte en vue de mieux réussir le transfert de connaissances. Enfin, la relation informelle entre les décideurs est apparue comme un facteur facilitateur du transfert de connaissances.Codes JEL : M160
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.012 |
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".