Efeitos tributários e seus reflexos no rendimento sobre investimento de pessoa física nas criptomoedas: um comparativo entre o Brasil e países do G-20
Bibliographic record
Abstract
As criptomoedas surgem cada dia com mais frequencia, e podem ser utilizadas desde para investimento ate para pagamentos, porem pelo fato desta ?moeda? nao ter nenhum orgao intermediario, demonstra necessidade de regulamentacao pelos paises e seus orgaos regulamentadores. A presente pesquisa objetiva analisar os reflexos tributarios nos rendimentos liquidos de pessoa fisica nas criptomoedas a partir do reconhecimento dado ou pela legislacao pertinente presente em cinco paises do G-20: o Brasil, o Canada, a Australia, o Reino Unido e os Estados Unidos, analisando as moedas digitais pela sua capitalizacao e tempo minimo de 4 anos no mercado, se encaixando para tal as seguintes moedas por ordem a Bitcoin, a Ethereum, a XRP, a Litecoin e a Monero. Para esse fim, o estudo utilizou a pesquisa documental e estudo de caso, de forma descritiva e por meio de elaboracao de pesquisa qualitativa. A partir disso, o estudo demonstrou que existe grande volatilidade nas criptomoedas e permitiu concluir que, apesar de ser a moeda pioneira, a Bitcoin nao foi a moeda mais rentavel do periodo de 2015 a 2018 e, da enfase a relevância do papel da tributacao no rendimento das moedas digital nos diferentes paises, que mesmo mantendo um padrao no reconhecimento das moedas digitais, apresentam divergencias nas aliquotas, refletindo diretamente na rentabilidade liquida das criptomoedas.(sic)
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".