Planification fiscale personnelle : Les enjeux actuels de la rémunération
Bibliographic record
Abstract
Cet article traite des principales catégories de programmes de rémunération aux fins de l'impôt et décrit quelques-unes des conséquences fiscales que peuvent avoir certains changements apportés à ces programmes de rémunération, ainsi que des moyens d'atténuer ces effets fiscaux. Il examine également les modifications législatives proposées être apportées dans le domaine des options d'achat d'actions, et propose des mesures que les entreprises devraient prendre en prévision de ces modifications. En outre, dans le contexte de la pandémie de COVID-19, cet article aborde les aspects clés du [laquo] travail de n'importe où [raquo] relativement à certains programmes de rémunération, ainsi que l'effet éventuel et attendu des impératifs environnementaux, sociaux et de gouvernance des sociétés dans la conception et la détermination de la rémunération des dirigeants.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".