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.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".