Bibliographic record
Abstract
En Ontario, comme dans d'autres parties du Canada, la bière est sujette à un taux de taxe d'accise moins élevée si elle provient d'une micro-brasserie. Nous examinons l'impact de cette politique sur le bien-être et estimons à quel point celle-ci est plus régressive qu'une taxe uniforme sur la bière. Nous utilisons des données uniques au point de vente sur les ventes et le prix au détail de bières de 2010 à 2015, en les faisant correspondre aux données sur les ménages à proximité de l'emplacement de chaque magasin. Comme prévu, la proportion de bières de micro-brasserie vendues augmente avec le revenu des ménages. Nos estimations indiquent que, pour un ménage dont le revenu annuel est de 50 000 $, le taux d'imposition effectif est de 0,20 cent plus élevé par litre que celui pour un ménage ayant un revenu de 250 000 $. Les taxes d'accise sur la bière de micro-brasseries et la bière qui n'en provient pas sont d'environ 20 et 70 cents par litre, respectivement.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".