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Record W4328135592

ENFOQUES ALTERNATIVOS PARA ENTENDER LAS RELACIONES INTERNACIONALES DE LOS ACTORES LOCALES EN AMÉRICA LATINA

2018· article· es· W4328135592 on OpenAlexaff
José Manuel Leal, Rocío Melendrez

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicPublic Policy and Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolitical scienceHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

El artículo discute dos enfoques utilizados en el estudio de las relaciones internacionales de los actores locales en América Latina, el liberal institucionalista y el de Política Verde (PV). Usualmente, la literatura en el tema analiza su participación en la arena internacional desde las escuelas tradicionales de las Relaciones Internacionales (RRII), como la liberal institucionalista, que pone al Estado en el centro del análisis y, por consecuencia ofrece una concepción centralizada del poder en las RRII. Esto ha impedido profundizar en el tema y entender de una manera más integral la acción internacional de los actores locales, es decir, como protagonistas y no actores pasivos. Por otro lado, enfoques alternativos, como es el caso de la teoría verde, incluye en el análisis la descentralización del poder y la desterritorialización de las RRII. De esta manera el enfoque de PV contribuye con nuevas perspectivas en viejos debates en la disciplina sobre soberanía y poder en el sistema internacional. Contrastando la escuela liberal institucionalista, con la llamada Política Verde, el articulo pretende así mostrar limitantes de las escuelas clásicas y empujar a futuras discusiones que contribuyan a ampliar nuestro conocimiento sobre las relaciones internacionales de actores locales.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.010
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.260
GPT teacher head0.603
Teacher spread0.344 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
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

Citations1
Published2018
Admission routes1
Has abstractyes

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