Comportamento dos termos do meio ambiente em textos de vulgarização
Bibliographic record
Abstract
Este artigo apresenta os resultados da análise do comportamento dos termos do meio ambiente em textos de vulgarização científica. Realiza-se um estudo léxico-semântico do vocabulário do meio ambiente utilizado pela imprensa <em>online</em> brasileira, cujos resultados são comparados com os usos dos termos por especialistas da área. Para isso, foram compilados dois <em>corpora</em>, um de textos jornalistícos e outro de textos científicos, cada um com cerca de 140 mil ocorrências. A comparação do funcionamento dos termos na língua de especialidade com seus usos na vulgarização possibilitou a observação dos “movimentos migratórios” das palavras, contribuindo para uma melhor compreensão dos mecanismos de terminologização e de <em>desterminologização</em>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".