Bibliographic record
Abstract
In this paper we examine how the presence of international tax evasion affects the choice of a foreign tax credit by a capital exporting region. Since the credit raises the opportunity cost of concealing foreign source income, it can be employed to discourage evasion activity. International tax evasion can thus help to rationalize the adoption of a tax credit in excess of a deduction‐equivalent rate. JEL Classification: H21, H26 Evasion fiscale pour le capital international et le problème du crédit d'impôt pour le fardeau fiscal à l'étranger. Ce mémoire examine comment la présence d'évasion fiscale pour le capital international affecte le choix du crédit d'impôt pour le fardeau fiscal à l'étranger par une région qui exporte du capital. Puisque le crédit d'impôt accroît le coût d'opportunité du camouflage de la source étrangère de revenus, c'est une technique qui peut être employée pour décourager l'évasion fiscale. Voilà qui peut expliquer qu'on adopte un crédit d'impôt qui est plus généreux que ce qui constituerait la déduction dans un système où le fardeau fiscal à l'étranger est simplement déduit du revenu imposable.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".