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Los estudios sobre la Unidad Popular en Chile en el nuevo milenio. ¿Están en deuda los historiadores?

2021· article· es· W3181788618 on OpenAlexaff
José Del Pozo Artigas, Danny Monsálvez Araneda, Mario Valdés Urrutia

Bibliographic record

VenueRadical Americas · 2021
Typearticle
Languagees
FieldSocial Sciences
TopicPolitical and Social Dynamics in Chile and Latin America
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceAllende meteoriteArtPhysics

Abstract

fetched live from OpenAlex

En este artículo se examinan los cuestionamientos señalados por algunos historiadores a principios del siglo XXI sobre que estarían en deuda con el estudio del gobierno de Allende y de la Unidad Popular (UP) en Chile (1970-3). Si bien los historiadores han investigado los diversos temas y problemas de ese período con diversa profundidad, hay temas que no se han abordado en su totalidad: por ejemplo, la relación entre socialistas, comunistas y el presidente Allende, y la participación de mujeres de izquierda, nativos y jóvenes en el país. el referido proceso histórico. Sin embargo, este trabajo aborda los aportes sobre el tema realizados principalmente por autores chilenos en libros y artículos sobre la UP, a saber: estudios generales del período, trabajos sobre Allende y algunos de sus colaboradores cercanos, los cambios económicos que afectaron intereses privados nacionales e internacionales, algunos de las fuerzas de Izquierda y Derecha (partidos y movimientos), sectores sociales populares, el Estado golpista, los militares, la cultura y la prensa. Un aspecto novedoso en un número significativo de estos trabajos es el uso de entrevistas con testigos que jugaron un papel significativo o vivieron el período de la UP.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0070.007
Scholarly communication0.0080.009
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.001

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 head0.010
GPT teacher head0.326
Teacher spread0.316 · 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 designQualitative
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

Citations2
Published2021
Admission routes1
Has abstractyes

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