HISCO (Historical International Standard Classification of Occupation): construindo uma codificacao de ocupacoes para o passado brasileiro
Bibliographic record
Abstract
Uma importante dimensao a ser considerada na construcao de normalizacoes para os estudos do trabalho no passado brasileiro diz respeito a codificacao das ocupacoes declaradas na documentacao pre-censitaria ou nos primeiros censos nacionais (sobretudo aqueles anteriores a 1940). Pretende-se apresentar a versao brasileira, ainda em construcao, da base de dados de classificacao de ocupacoes denominada RISCO. Tal base de dados pretende adaptar, para periodos historicos, a International Standard Classification of Occupation (ISCO), que e a base da Classificacao Brasileira de Ocupacoes (CBO), utilizada contemporaneamente pelo Instituto Brasileiro de Geografia e Estatistica (IBGE) e outros orgaos produtores de estatisticas. Trata-se de um projeto internacional envolvendo historiadores e cientistas sociais de diversos paises (Belgica, Gra-bretanha, Canada, Franca, Alemanha, Holanda, Noruega, Suecia, Portugal, Espanha, Dinamarca, India, Russia, Filipinos e Grecia). A insercao brasileira toma-se relevante tendo em vista o desenvolvimento posterior de estudos comparados, tanto ao tempo quanto ao espaco, ja que se parte de uma mesma logica de codificacao para classificar as ocupacoes de diferentes periodos historicos e de diversos paises. A base de dados brasileira toma como ponto de partida listas de habitantes do seculo 19, sobretudo Sao Paulo, em 1836, mas tambem outras fontes de outros estados brasileiros.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".