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Record W4281608714 · doi:10.3389/frma.2022.898896

Mexican Scientist Diaspora in North America: A Perspective on Collaborations With México

2022· article· en· W4281608714 on OpenAlexaffabout
Paulina Gómez-Flores, Vicente Morales-Salgado, Angélica Maza, Aline Villarreal, Linda R. Lara-Jacobo, Mónica I. Jiménez-Córdova, Daniel Jiménez-Alvarez, Alma Cristal Hernández‐Mondragón

Bibliographic record

VenueFrontiers in Research Metrics and Analytics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDiasporaWork (physics)Perspective (graphical)Political scienceEconomic growthDeveloping countrySociologyDevelopment economicsLaw

Abstract

fetched live from OpenAlex

Scientific diasporas from developing countries represent an opportunity to strengthen international collaborations. These collaborations build upon the desire of members of the diasporas to establish scientific, academic, technological, and cultural exchange networks with the communities in their country of origin. While Mexico has a significant number of scientists residing abroad, particularly in North America, and most of them are committed to aid in the country's development, institutional coordination has not harnessed its benefits. In this work, we present an analysis of initiatives carried out by Mexican scientists, members of the diaspora, studying or working in the United States of America and Canada. The study is based on a set of interviews with members of this diaspora. We asked scientists about the conditions that enabled or obstructed their initiatives back in Mexico, and we discussed the role of these factors for capacity building. We also provide general recommendations to enhance contributions to the advancement of science in the country.

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)0.004
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0150.004
Scholarly communication0.0060.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.201
GPT teacher head0.495
Teacher spread0.294 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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