Public policies in science, technology, and innovation: a benchmark for measuring development
Bibliographic record
Abstract
Introduction: Public policies focused on science, technology, and innovation (STI) reflect states' capacity to adapt to scientific progress and compete internationally. Methods: A literature review was conducted using databases (Scopus, SciELO, Dialnet) and reports from international organizations such as ECLAC and the Science and Technology Indicators Network. Results: Global R&D investment is led by Asia (41.6%) and the United States-Canada (30.5%). Latin America and the Caribbean (LAC) accounts for only 2.32% of global spending. Furthermore, in LAC, funding comes primarily from the state, unlike in China, the United States, the European Union, and the OECD, where investment from the private sector prevails. Regional indicators show low R&D spending, limited funding, and reduced patent generation, especially in health and areas related to sustainability. The first public STI policy in the region demonstrates a limited trajectory and uneven integration into the global scientific system. Conclusions: Latin America and the Caribbean (LAC) shows poor performance in science, technology, and innovation (STI), with insufficient levels compared to developed economies, which demands priority attention and strengthening of public policies that promote scientific and technological progress.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.039 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".