Seminário do CCSA-UFRN: mapeamento temático das produções científicas na área de Ciência da Informação
Bibliographic record
Abstract
Analisa a produção científica da Ciência da Informação apresentada no Seminário de Pesquisa do Centro de Ciências Sociais Aplicadas (CCSA) da Universidade Federal do Rio Grande do Norte (UFRN). Como objetivos têm-se o de identificar as temáticas mais abordadas pelos discentes e docentes da Ciência da Informação, tendo como recorte os anos de 2012 a 2015. Utiliza-se o estudo cientométrico e a pesquisa quantitativo-descritiva, que a partir de dados estatísticos, busca-se verificar as temáticas e a quantidade produzida nos referidos anos. A partir do mapeamento de vinte e nove trabalhos, conclui-se que as temáticas – Acessibilidade e Usabilidade – são as mais pesquisadas, seguidas de Marketing, Biblioteca, Web 2.0, Arquitetura da Informação e Fontes de Informação.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".