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Record W4362581830 · doi:10.3390/publications11020022

Constraints on Research in Biological and Agricultural Science in Developing Countries: The Example of Latin America

2023· article· en· W4362581830 on OpenAlexafffund
C.S. Galina, José F. Martínez, Bruce D. Murphy

Bibliographic record

VenuePublications · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLatin AmericansAgricultureGlobePublicationQuality (philosophy)Political scienceEconomic growthRegional sciencePublic relationsSociologyGeographyEconomicsLawPsychology

Abstract

fetched live from OpenAlex

Science is an international effort, receiving contributions from researchers across the globe. The capacity of a country or a region to generate and publish quality research varies greatly according to the location examined. Among the factors that dictate the quantity and quality of scientific research are the availability of infrastructure and human resources, the traditions related to research endeavors, and, most significantly, local governmental support for research. There are several conditions that both individually and cooperatively limit research activities in Latin America, such as insufficient governmental support, a paucity of material and technical resources, heavy teaching loads, the absence of peer networks, and multiple constraints on publication. This commentary has been developed to discuss each of the issues that permit and, more frequently, limit biological and agricultural research endeavors in Latin America.

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)0.011
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.013
Science and technology studies0.0070.011
Scholarly communication0.0130.005
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.836
GPT teacher head0.609
Teacher spread0.227 · 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 designObservational
DomainIncentives
GenreEmpirical

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

Citations6
Published2023
Admission routes2
Has abstractyes

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