Scientometric Analysis of Research Output from Brazil in Response to the Zika Crisis Using e-Lattes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose This paper aims to test the use of e-Lattes to map the Brazilian scientific output in a recent research health subject: Zika Virus. Design/methodology/approach From a set of Lattes CVs of Zika researchers registered on the Lattes Platform, we used the e-Lattes to map the Brazilian scientific response to the Zika crisis. Findings Brazilian science articulated quickly during the public health emergency of international concern (PHEIC) due to the creation of mechanisms to streamline funding of scientific research. Research limitations We did not assess any dimension of research quality, including the scientific impact and societal value. Practical implications e-Lattes can provide useful guidelines for different stakeholders in research groups from Lattes CVs of members. Originality/value The information included in Lattes CVs permits us to assess science from a broader perspective taking into account not only scientific research production but also the training of human resources and scientific collaboration.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.033 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.025 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it