Science, Innovation, Communication and Ethics in the days of COVID-19 in Latin America
Bibliographic record
Abstract
Two renowned Cuban scientists and professors who arrived in Brazil in the last decade of the last century, completed their doctorates at USP and expanded their professional achievements in the country, share in a relaxed way their knowledge and experiences about Science, Technology, Communication and Ethics in the times of COVID-19 in Latin America. One of them from the Exact Sciences area, Efrain Pantaleón Matamoros; and the other from Social Sciences, Felipe Chibás Ortiz. The views of these two Latin American researchers - who have previously written an article together on Innovation Management - now speak of these themes from the perspectives of different sciences, in an enriching way – relevant to the context of the ‘new-normal’ during the times of Covid-19. This article, presented as an interview, reflects on what these two experts have to say about Science, Technology, Innovation, Communication and Ethics in the days of COVID-19 in Latin America, from their unique perspectives.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".