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Record W2979453893 · doi:10.4000/laboreal.13117

Formadores en la indústria avícola : actores clave en situación difícil

2006· article· es· W2979453893 on OpenAlexaff
Céline Chatigny

Bibliographic record

VenueLaboreal · 2006
Typearticle
Languagees
FieldSocial Sciences
TopicEducational Practices and Sociocultural Research
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesPolitical scienceICTSArtInformation and Communications Technology

Abstract

fetched live from OpenAlex

El presente estudio exploratorio trata de las dinámicas entre los actores implicados en la formación a las distintas tareas en tres empresas del sector avícola quebequense : responsables de formación, formadoras o formadores, y aprendices (Chatigny et al., 2005). El principal objetivo es la identificación de los obstáculos a la formación para el desarrollo integrado de las competencias vinculadas con las tareas así como a la salud y la seguridad en el trabajo. Los datos provienen de grabaciones video de entrevistas realizadas en el marco de un estudio realizado por Richard et al. (2002) en tres empresas. Algunas entrevistas complementarias nos han permitido contextualizar y completar el análisis de los datos. Los resultados indican que los formadores desempeñan varios papeles a fin de crear y mantener dinámicas favorables para la formación y el aprendizaje. Son actores claves que interactúan con los aprendices y los responsables de la empresa, tanto en los aspectos relacionados con el aprendizaje como con la salud y seguridad laboral, así como con la gestión de la formación y de la producción. Las empresas participantes reconocen la importancia de hacer evolucionar los dispositivos de formación, pero al mismo tiempo tienden a mantener una lógica Tayloriana de las situaciones de trabajo y aprendizaje de los aprendices y los formadores.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.007
Scholarly communication0.0090.005
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.014
GPT teacher head0.386
Teacher spread0.372 · 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 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
Published2006
Admission routes1
Has abstractyes

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