LAATS, A.; SIEGEL, H. Teaching evolution in a creation nation. Chicago: The University of Chicago Press, 2016.
Bibliographic record
Abstract
Gabriel Dall' Alba 2Uma das controvérsias socioculturais mais notórias e antigas relacionadas ao ensino de Ciências diz respeito ao papel da teoria da evolução -e de suas pretensas alternativas -no currículo de escolas públicas.Deveriam as aulas de Ciências ou Biologia tratar somente da teoria da evolução ou os professores também deveriam apresentar ideias como o criacionismo e o Design Inteligente (DI) como outras potenciais explicações científicas para a diversidade dos seres vivos?O historiador Adam Laats e o filósofo Harvey Siegel discorrem sobre essas e outras questões relacionadas em Teaching Evolution in a Creation Nation (The University of Chicago Press), de 2016.Laats e Siegel organizaram a obra em oito capítulos, dos quais os primeiros quatro são destinados a uma análise histórica das controvérsias que têm envolvido o ensino da teoria da evolução biológica desde a década de 1920 nas escolas públicas dos Estados Unidos, enquanto os quatro capítulos finais trazem algumas das principais discussões filosóficas suscitadas
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".