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Construcción del camino de impacto

2023· book-chapter· es· W4391700274 on OpenAlexaff
Michel Cot, Laure Emperaire, Isabelle Henry, Laurent Laplaze, François Roubaud, Florence Sylvestre, Jean‐Daniel Zucker

Bibliographic record

VenueIRD Éditions eBooks · 2023
Typebook-chapter
Languagees
FieldSocial Sciences
TopicViolence, Education, and Gender Studies
Canadian institutionsImpact
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

El camino de impacto proporciona el marco para analizar el estudio. Sirve de hilo conductor para llevar a cabo el análisis y presentar los resultados en forma de informe, que se organiza siguiendo la estructura del camino de impacto. El camino de impacto incluye los datos de contexto que influyen en su desarrollo y consta de cinco fases: la contribución de los actores, que identifica las investigaciones objeto del estudio de caso, los actores implicados y sus respectivas contribuciones; los ...

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.018
Scholarly communication0.0100.009
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.049
GPT teacher head0.326
Teacher spread0.277 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueIRD Éditions eBooksSame topicViolence, Education, and Gender StudiesFrench-language works237,207