Produção científica docente em tratamento temático da informação no Brasil: uma abordagem métrica como subsídio para a análise do domínio
Bibliographic record
Abstract
Aiming at characterizing the scientific production of professors in the area of Subject Treatment of Information – S.T.I., 77 Journal articles by 19 post-graduation professors acting in the field, were selected, so as to detect the characteristics of this production and information sources utilized by authors- professors which may provide a wider knowledge of the area as an academic acting space. From the information provided by these articles, a database encompassing fields such as author, title of the article, title of publication, keywords, citations, country, language, year of publication and institution of the author, was compiled. The VantagePoint software, which organizes the information allowing the generation of lists and data-crossing matrices, which in turn enable one to verify the existing relationships among the fields, was used. As for the productivity of authors-professors who are distributed in 5 universities (USP, UNESP, UFMG, UnB and UFF), a data analysis showed that group kept an average of 4 to 5 articles per year, from 1990 to 2006. These articles were published in four languages – Portuguese, English, Spanish and France – in 27 Journals of 9 countries, being 18 published in Brazil and a strong trend to publish in renowned and high quality Journals was seen. An analysis of the 817 references contained in the articles, revealed a total of 659 authors cited and a 50% preference for books, as an information source, as well as the predominance of classical authors of the area, used as a theoretical base. In addition, some 20% of the professors’ production in the literature used in the area, was identified, thus characterizing the impact and importance of this production for the field. The subjects approached were identified from the key-words utilized, demonstrating a predominance of interest for the processes of the area, being indexing the most frequent subject.
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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.047 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".