Public policies in science, technology, and innovation: a benchmark for measuring development
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
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.
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.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.055 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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