Survol des fondements théoriques de l’hypothèse de Porter
Bibliographic record
Abstract
Cet article présente de manière non technique certains des fondements théoriques possibles de l’hypothèse de Porter selon laquelle, des réglementations environnementales strictes peuvent améliorer le profit des industries qui y sont soumises. Après une brève présentation de l’hypothèse, les arguments basés sur l’existence d’imperfections au sein de l’entreprise sont passés en revue. Les imperfections du marché susceptibles d’éventuellement justifier l’hypothèse de Porter sont ensuite discutées. Les principales conclusions de ce survol sont : i) l’hypothèse de Porter requiert l’interaction de l’externalité environnementale avec au moins une autre source de distorsions; ii) le type d’intervention publique qui peut aboutir à un effet à la Porter dépend de la nature des distorsions qui interagissent, l’atteinte de l’optimum peut exiger l’usage de plusieurs instruments; iii) l’exploration empirique de l’hypothèse de Porter doit, pour être valide, autoriser la présence de ces multiples distorsions.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".