Menos e melhores tributos: princípios para o corte de tributos e para a reforma tributária.
Bibliographic record
Abstract
Neste trabalho, as condicoes pelas quais o Canada pode obter menos e melhores tributos sao revistas. Em primeiro lugar, e questionado que se quisermos ter menores tributacoes e imperioso que a economia seja, da melhor forma possivel, colocada e mantida dentro de seu maximo potencial de emprego, que sejam diminuidos os custos dos bens e dos servicos publicos e, tambem, que seja mantida uma reducao gradual da divida publica em relacao ao produto interno bruto. Em segundo lugar, argui-se que se quisermos melhores tributos, deve haver uma harmonia entre a equidade e a eficiencia economica. A equidade sera alcancada quando os cortes atingirem a tributacao dos rendimentos das pessoas fisicas e juridicas, da renda media dos contribuintes e das familias. Eficiencia e crescimento economicos serao garantidos se os cortes enfatizarem esforco produtivo, economias e investimentos, alem da competitividade fiscal internacional. Diversas medidas concretas para atingir tais objetivos sao propostas. Um compromisso politico entre equidade e eficiencia sera alcancado somente se as tributacoes em relacao as pessoas fisicas e juridicas forem reformadas simultaneamente.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".