Tendências da pesquisa brasileira em Ergologia
Bibliographic record
Abstract
This article aims to analyze trends in research on Ergology in Brazil published from 1997 to 2019, considering the nature of the publication and the potential impact of the databases in which they are published. Studies related to occupational health indicate the growing influence of Ergology in the understanding of the world of work, a fact evidenced in previous bibliometric research, of lesser breadth and scope of the databases investigated, which is why we intend to cover gaps and broaden the analysis with more up-to-date data. Using descriptors peculiar to Ergology, surveys were conducted in the Web of Science, Scopus and SciELO databases, in journals not indexed to the databases cited, and in academic productions available in the Capes catalog. The projection of Brazil in studies on Ergology was revealed, with the Southeast being the region with the highest concentration of authors and volume of publications. However, the interrelationship between researchers tends to be limited to the institutions in which they work. There is a prevalence of theses and dissertations to the detriment of articles in productions related to Ergology. Publications point to interdisciplinarity - with a predominance of Occupational Health, Education, and Psychology - and tend to feature in vehicles of lesser scientific relevance.
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 | 0.019 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.032 | 0.054 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".