Learning from the tigers- comparing innovation institutions in rapidly developing economies with Latin America
Bibliographic record
Abstract
Este artículo compara las políticas e instituciones de innovación en Latinoamérica con las economías e instituciones de los tigres de Asia Oriental y de Europa. Encontramos que hay nítidas diferencias en las políticas sobre recursos, priorización y organización de la ciencia y la tecnología entre Latinoamérica y las de los tigres, que tal vez ayuden a explicar el decepcionante desempeño económico de la región en las décadas recientes. El artículo sugiere que es necesaria una sistemática reorganización de la política de innovación en Latinoamérica. Las concordancias de los tigres sugieren que una similar reevaluación no solamente debe considerar niveles de recursos, sino también nuevos marcos institucionales para priorizar, coordinar y comercializar nuevas tecnologías generadoras de mayores beneficios para toda la economía y la sociedad.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".