Bibliographic record
Abstract
Adriana BauerUniversidade de São Paulo-USP, São Paulo, SP; BrasilFundação Carlos Chagas-FCC, São Paulo, SP; Brasilhttp://orcid.org/0000-0002-5942-9181Ana Maria EyngPontifícia Universidade Católica do Paraná-PUC-PR, Curitiba, PR; Brasilhttp://orcid.org/0000-0003-0224-5880Andrea Paula de Souza WaldhelmFaculdade de Filosofa, Ciências e Letras de Macaé-FAFIMA, Macaé, RJ; BrasilArtur Marecos Parreira e Moreira GonçalvesUniversidade Lusófona de Humanidades e Tecnologias, Lisboa; Portugalhttp://orcid.org/0000-0001-6721-292XBertha de Borja Reis do ValleUniversidade do Estado do Rio de Janeiro-UERJ, Rio de Janeiro, RJ; BrasilFátima Kzam Damaceno de LacerdaUniversidade do Estado do Rio de Janeiro-UERJ, Rio de Janeiro, RJ; BrasilIvár César Oliveira de VasconcelosUniversidade Paulista-UNIP, Brasília, DF; Brasilhttp://orcid.org/0000-0001-5186-8000João Casqueira CardosoUniversidade Fernando Pessoa-UFP, Porto, ; Portugalhttp://orcid.org/0000-0002-0894-452XLeonardo Amaro Nolasco da SilvaUniversidade do Estado do Rio de Janeiro-UERJ, Rio de Janeiro, RJ; Brasilhttp://orcid.org/0000-0001-9814-259XMarcelo Arancibia HerreraUniversidade Federal de Pernambuco-UFPE, Recife, PE; BrasilUniversitat Oberta de Catalunya-UOC, Barcelona; Españahttp://orcid.org/0000-0002-4314-4253Sergio Luiz Pereira da SilvaUniversidade Federal do Estado do Rio de Janeiro-UNIRIO, Rio de Janeiro, RJ; Brasilhttp://orcid.org/0000-0002-9417-4380Virgínio Isidro Martins de SáUniversidade do Minho-UMinho, Braga; Portugalhttp://orcid.org/0000-0002-1941-8664
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.234 | 0.160 |
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".