Revue de la littérature sur les programmes de dénonciation fiscale
Bibliographic record
Abstract
Les programmes de dénonciation encouragent la divulgation de pratiques illégales, immorales ou illégitimes aux autorités compétentes et ont montré leur efficacité dans l’encouragement de l’exposition de la fraude au sein des organisations. Les programmes de dénonciation fiscale ont été de plus en plus adoptés par les autorités fiscales nationales dans le but de lutter contre les comportements fiscaux agressifs. En raison de la popularité croissante de ces programmes, l’identification des facteurs qui affectent l’efficacité des programmes de dénonciation fiscale est essentielle, tout comme le développement de notre compréhension des domaines dans lesquels la recherche sur les programmes de dénonciation fiscale reste à explorer. C’est dans cette perspective que nous réalisons une revue de la littérature sur la dénonciation fiscale afin d’examiner la littérature existante sur les programmes de dénonciation fiscale et d’identifier quels facteurs des programmes de dénonciation fiscale n’ont pas encore été explorés.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".