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Record W4251093719 · doi:10.4000/laboreal.400

Violencia y trabajo

2017· article· es· W4251093719 on OpenAlexaff
Laerte Idal Sznelwar

Bibliographic record

VenueLaboreal · 2017
Typearticle
Languagees
FieldSocial Sciences
TopicLabor Law and Work Dynamics
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsPsychologyPolitical science

Abstract

fetched live from OpenAlex

Tratar la cuestión de la violencia en el trabajo es un gran desafío, en tanto existen toda una serie de discusiones y polémicas acerca de cómo definir qué es la violencia y cómo distinguirla de otro tipo de fenómenos relacionados con las relaciones humanas.En el caso específico de muchas personas que trabajan, diferentes modos de relación moldeados por la organización del trabajo son, de alguna manera, fuente de sufrimiento patógeno.Sin embargo, no es posible categorizar todos los tipos de insatisfacción como violencia, en tanto existe un riesgo elevado de banalización y pérdida del poder de caracterización y de comprensión de sus orígenes y de como se disemina en los ambientes de trabajo.Caracterizar aquello que entendemos por violencia en un determinado contexto es importante para poder responsabilizar efectivamente a quienes, de alguna manera, actúan violentamente.

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)0.008
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: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.012
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.012
GPT teacher head0.326
Teacher spread0.314 · 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
Published2017
Admission routes1
Has abstractyes

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