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Record W7010449674

Identidades compartidas: la centralidad de los lazos culturales como motor paradiplomático

2016· article· es· W7010449674 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2016
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Context (archaeology)Work (physics)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

El presente artículo tiene por objetivo analizar la incidencia del factor identitario en las \nactividades paradiplomáticas. Para ello, se ha desarrollado un marco de pensamiento \na partir de la adaptación de ciertas teorías de las Relaciones Internacionales al campo \nde la paradiplomacia. Asimismo, se introduce una propuesta metodológica por medio \nde categorías analíticas, la cual ha sido aplicada a tres casos de estudio que, debia sus particularidades, sirven para ilustrar este fenómeno: la relación de Quebec con \nFrancia, las colectividades vascas francesas con respecto al País Vasco y Navarra, \ny el Gobierno Regional de Kurdistán en relación con las localidades kurdas de los \nEstados vecinos a Iraq. Así, el análisis teórico-empírico ayudará a entender de qué \nmodo la elección del actor con el que se pretende colaborar parte de una identificación \nrecíproca, esto es que se sienten parte de una misma colectividad. Lo anterior no sólo \npuede motivar la relación paradiplomática sino que incluso puede ser una finalidad, \nla cual se traduce en acuerdos de preservación respecto a la cultura compartida por \nambas partes. Sin embargo, las peculiaridades de cada caso evidenciarán que, aunque \nla identidad sirva como motor paradiplomático, existen otros factores que reducen \nsu potencial de cooperación. \n

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.335

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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 designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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