Estrategia para fortalecer las capacidades de investigación en salud en universidades públicas regionales: rol del canon y del Instituto Nacional de Salud
Bibliographic record
Abstract
The development of scientific health research requires a sustained and articulated research system that is consistent with the research priorities, as well as both internal and external funding, and availability of competent human resources. The Mining Canon, a constitutional right, has been partly used to foster applied scientific research in public universities (PU). In addition, the National Health Institute (INSTITUTO NACIONAL DE SALUD - INS) is devoted, among others, to promoting, managing and disseminating health research development at a national level. As part of these activities, a technical team was created to provide technical assistance to PU for research development using Mining Canon funds by making local adjustments to research protocols promoted by the INS and assumed by the professors-researchers at the Universities. This article aims at describing the reality of research at Peruvian public universities that have access to Mining Canon funds, as well as to elaborate on the work the INS is carrying out in order to strengthen research capabilities, starting with the development of research proposals that could potentially be funded by the Mining Canon.
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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 | 0.065 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.007 |
| 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".