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Record W1545379543

Pasado y Memoria. N. 12 (2013)

2004· article· es· W1545379543 on OpenAlexfundno aff
Pasado y Memoria

Bibliographic record

VenueRepositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · 2004
Typearticle
Languagees
FieldSocial Sciences
TopicSocial Sciences and Policies
Canadian institutionsnot available
FundersUniversidad de AlmeríaUniversidad de ExtremaduraUniversidad de SalamancaUniversitat Autònoma de BarcelonaUniversidad de ValladolidUniversitat Rovira i VirgiliUniversidad Rey Juan CarlosCore Research for Evolutional Science and TechnologyUniversità degli Studi di TriesteUniversidad de Castilla-La ManchaUniversidad Nacional de Mar del PlataUniversidad Carlos III de MadridCentral European UniversityUniversité de RouenUniversità di BolognaUniversidad Autónoma de MadridPrinceton UniversityNorthern Illinois UniversityUniversité de ToulouseUniversitat de ValènciaUniversidade de Santiago de CompostelaUniversidad de ZaragozaUniversitat de GironaUniversidad Complutense de MadridEuskal Herriko UnibertsitateaEuropean CommissionUniversity of CambridgeTrent UniversityUniversidad de CantabriaUniversidad de AlicanteUniversitat Pompeu FabraUniversidad de MálagaUniversidad Nacional de Educación a DistanciaUniversité Grenoble AlpesFlorida International UniversityUniversity of OxfordUniversidad de SevillaUniversidad de La LagunaUniversitat de BarcelonaTexas AgriLife ResearchUniversidad de GranadaUniversidad de NavarraUnited Nations
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Los Políticos Europeos y Napoleón = European Politicians and Napoleon / Rafael Fernández Sirvent y Vicente León Navarro (coords.)

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0790.037

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 head0.288
Teacher spread0.278 · 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
Published2004
Admission routes1
Has abstractyes

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