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Record W7005455077

Rehime. Cuadernos de la Red de Historia de los Medios, Año 1 no. 1

2011· article· es· W7005455077 on OpenAlexaboutno aff

Bibliographic record

VenueRepositorio Digital Institucional de la Universidad de Buenos Aires (Universidad de Buenos Aires) · 2011
Typearticle
Languagees
FieldSocial Sciences
TopicLatin American Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSocial impactPopulationSecretary general
DOInot available

Abstract

fetched live from OpenAlex

Indice: Encuesta | Historia de los medios en América Latina: Héctor Schmucler; Eduardo Romano; Omar Rincón; Andrea Matallana; Mónica Maronna; Micael Herschmann; Celia del Palacio; Esther Hamburger; Gilberto Eduardo Gutiérrez; Claudia Irene García Rubio; Luiz Artur Ferraretto; Luis César Díaz; Marialva Carlos Barbosa; Patricio Bernedo Pinto; Marco Antonio Villarruel. Dossier | Jorge B. Rivera; Presentación; Eduardo Romano, El salto inicial de Jorge B. Rivera; Jorge Lafforgue, Un kantiano rioplatense; Pablo Alabarces, Rivera, o la arqueología; Alejandra Laera, Por una historia menor de la literatura; Mirta Varela, Fundaciones y márgenes de la cultura; Ana Lia Rey, Las revistas en la historia (social) de Jorge B. Rivera; Laura Vazquez, Entre el margen, lo popular y la cultura. Las lecturas de Jorge Rivera sobre la historieta; Listado de obras (con la colaboración de Judith Gociol). Diálogo | Televisión: 60 años de historia sin archivos: Mateo Gómez Ortega, coordinador del Área de Tecnología y Sistemas de RTA, Javier Trímboli, asesor histórico de la TV Pública, Fernanda Ruiz, Maximiliano Tocco, Maida Diyarián y María Victoria Ojeda, integrantes del Área de Tecnología y Sistemas de RTA , y Mirta Varela, Ana Lía Rey, Fernando Ramírez Llorens, Máximo Eseverri, Florencia Luchetti y Paola Margulis, integrantes de ReHiMe.

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)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0030.004
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0390.014

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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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
GenreOther

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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