Bibliographic record
Abstract
Neste artigo, avançamos no debate sobre os cancelamentos examinando quem participa deles. Por "participar" compreendemos colaborar de qualquer maneira, com ou sem a intenção direta de atacar e envergonhar o infrator. A literatura atual assinalou a participação de contrapúblicos subalternos (Clark, 2020), do Estado e da imprensa (Trottier, 2018), criadores de conteúdo (Lewis y Christin, 2022), empregadores (Saint-Louis, 2021) e usuários habituais de redes sociais (Trottier, 2018; Bouvier, 2020). No entanto, não existe uma explicação geral para teorizar e excluir todos os possíveis participantes de um cancelamento. Considerando os cancelamentos como uma forma de aplicação de normas baseadas na vergonha, utilizamos o conceito de empreendimento (Becker, 1963) como central para compreender os canceladores como atores interessados no cancelamento devido a causas virtuosas ou oportunistas.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".