Les alliances technologiques stratégiques: de la théorie à la situation canadienne
Bibliographic record
Abstract
Technological cooperation between business enterprises has become common-place over the past ten years or so, following an increase in the uncertainty, risk, and costs of research and development brought about by growing international competition and the unsettling impact of data processing technologies (and to a lesser degree biotechnologies) throughout the entire industrial sector. Strategies in R&D cooperation, first adopted by Japanese corporations, were copied by European firms in the early 80s and then by American and Canadian corporations later on. Governments have got in on the action through policies for encouragement of collective R&D. Current theories in economies and business administration are not very useful for understanding this phenomenon. Neo-classical economies' assumption of perfect competition, as well as dissertations on product obsolescence and transaction costs, permeate theories in business administration and do not help us comprehend this new organizational phenomenon. We have, however, come across some crucial leads towards an explanation in certain models of imperfect competition and in managerial studies on informal cooperation by businesses in R&D.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".