Bibliographic record
Abstract
O reducionismo como uma abordagem à epistemologia do testemunho faz certas exigências àquele que recebe o testemunho, que a posição contrária, o anti-reducionismo, não faz. Depois de estabelecer as duas abordagens e suas respetivas exigências sobre aquele que recebe o testemunho, argumentamos que o reducionismo também faz determinadas exigências àquele que testifica, que o anti-reducionismo não faz. A dificuldade de decidir entre as duas abordagens conduz a uma preocupação com a extensão na qual o estado atual de coisas na epistemologia pode oferecer indicação segura sobre o tipo de exigências de anomimato que uma sociedade posta em rede faria àqueles que prestam testemunho. Argumentamos também que essa preocupação pode ser mitigada com o reconhecimento do fato de que as duas abordagens possuem uma base comum quando é preciso considerar casos de conflito de testemunhos.
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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".