Bibliographic record
Abstract
Lorsque les ecoles se deteriorent, qu'une clinique ferme, qu'un viaduc s'effondre, qu'un festival perd une subvention, c'est a cause des paradis fiscaux. Source d'inegalites croissantes et de pertes fiscales colossales, le recours aux paradis fiscaux par les grandes entreprises et les particuliers fortunes explique en grande partie les politiques d'austerite. «C'est legal!», claironnent les fautifs. En cinq chapitres d'une redoutable efficacite, Alain Deneault souleve la question politique de cette escroquerie legalisee. Comment les definir, quelles sont les consequences dramatiques de cette spoliation et comment contrer cette loi des puissants? Il est urgent de mettre fin a ce mecanisme insense par lequel les contribuables financent les banques par le service de la dette et les services publics dont profitent les entreprises, tout en s'appauvrissant. Il faut se saisir collectivement de la question des paradis fiscaux pour que ce qui est legalise ne le soit plus. En partenariat avec la revue Liberte, Oxfam-Quebec et Echec aux paradis fiscaux.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 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".