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Latin American Science: Much Work Remains

2013· letter· en· W2314306412 on OpenAlexaboutno aff
Jorge A. Huete‐Pérez

Bibliographic record

VenueScience · 2013
Typeletter
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansWork (physics)Engineering ethicsPolitical scienceEngineeringLawMechanical engineering

Abstract

fetched live from OpenAlex

![Figure][1] CREDIT: ISTOCKPHOTO At a time when remarkably few Latin American countries are improving their research capacity, it seems incongruous to display much enthusiasm about the future of science, technology, and innovation in the region. The Editorial Growing Latin American asserts that the region is swiftly improving its research capacity (C. R. S. Garcia et al. , 30 November 2012, p. [1127][2]). By not clarifying that only a handful of Latin American countries are experiencing scientific growth, the optimism reflected in the Editorial is somewhat misleading. Despite increasing its share of publications from 1 to 4% in the past 30 years, Latin America remains a small player on the world scale, ranking behind Europe, Asia, North America, and the Middle East ([ 1 ][3]). In Latin America, scientific growth is far from uniform. Brazil, Mexico, Argentina, Chile, and Colombia combined contribute 95% of all scientific publications in the region ([ 2 ][4]). The remaining countries lag far behind the rest of the world. Insufficient investment in R&D has resulted not only in subpar academic quality, but also in minimal synergy between industry and universities. In 2009, investment in R&D in Latin America was equivalent to 0.69% of GDP, compared to 2.40% in Organization for Economic Co-operational and Development (OECD) countries ([ 3 ][5]). In some countries in the region, meager increases in public spending on R&D reflect an overall increase in spending as the economies grow, rather than a greater ratio in R&D. Although some training fellowships have been established in the region, they are generally limited to the largest economies. To move from scientifically lagging to scientifically proficient, smaller countries need to create a weightier pool of scientists, invest more resources, and strengthen their science programs. Most important, they would benefit from improved science, technology, and innovation strategies to lead them toward knowledge-based economies. Although positive news regarding science in powerhouse nations such as Brazil is encouraging, we shouldn't be blind to the fact that much remains to be done to advance science in Latin America. 1. [↵][6] 1. E. Archambault , Thirty years in science: Secular movements in knowledge creation (Montreal, Quebec, Monograph, Science-Metrix, 2010); [www.science-metrix.com/30years-Paper.pdf][7]. 2. [↵][8] The SCImago Journal & Country Rank ([www.scimagojr.com][9]). 3. [↵][10] RICYT (Network for Science and Technology Indicators), Database of Indicators (2011); [www.ricyt.org][11] [in Spanish]. [1]: pending:yes [2]: /lookup/doi/10.1126/science.1232223 [3]: #ref-1 [4]: #ref-2 [5]: #ref-3 [6]: #xref-ref-1-1 View reference 1 in text [7]: http://www.science-metrix.com/30years-Paper.pdf [8]: #xref-ref-2-1 View reference 2 in text [9]: http://www.scimagojr.com [10]: #xref-ref-3-1 View reference 3 in text [11]: http://www.ricyt.org

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.994
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.005
Science and technology studies0.0070.005
Scholarly communication0.0200.013
Open science0.0020.007
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.2760.201

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.

Opus teacher head0.012
GPT teacher head0.270
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreCommentary

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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