Bibliographic record
Abstract
This paper reviews the treatment of intellectual property rights in the North American Free Trade Agreement (NAFTA) and considers the welfare‐theoretic bases for innovation transfer between member and nonmember states. Specifically, we consider the effects of new technology development from within the union and question whether it is efficient (in a welfare sense) to transfer that new technology to nonmember states. When the new technology contains stochastic components, the important issue of information exchange arises and we consider this question in a simple oligopoly model with Bayesian updating. In this context, it is natural to ask the optimal price at which such information should be transferred. Some simple, natural conjugate examples are used to motivate the key parameters upon which the answer is dependent. L'article que void analyse comment I' Accord de libre‐échange nord‐américain (ALENA) traite la protection de la propriété intellectuelle et s‘attarde sur les principes théoriques du bien‐Aêtre résultant du transfert de I' innovation entre etats membres et non membres. Plus précisément, I'auteur examine les consequences de I'élaboration d'une nouvelle technologie au sein de I'union économique et s'interroge sur I'efficacité (sous I'angle du bien‐être social) du transfert de cette technologie aux états non membres. L'importante question du partage de l'information surgit dès que la nouvelle technologie inclut des elements stochastiques. L'auteur étudie cette question en prenant pour modèle un simple oligopole actualisé par la méthode bayesienne. Dans un tel contexte, il est naturel de réclamer le prix optimal auquel il devrait y avoir partage de I' information. Quelques exemples simples, à conjugué naturel, servent à faire ressortir les principaux paramètres sur lesquels repose la réponse è la question examinée.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".