VIOLÊNCIAS NO AMBIENTE DE TRABALHO: PONDERAÇÕES TEÓRICAS
Bibliographic record
Abstract
Resumo Neste artigo teórico, tem-se como objetivo analisar as diferenças e possíveis sobreposições conceituais a respeito de tipologias de violências no trabalho, as quais são cada vez mais sutis. A reflexão crítica sobre o trabalho de Hershcovis (2011) nos instigou a pensar nas diferenciações entre as tipologias de violência, visando sua compreensão a partir de modelos que se aproximam de uma visão mais complexa e realística das interações sociais (Andersson & Pearson, 1999; Cortina, Kabat-Farr, Magley, & Nelson, 2017; Leymann, 1996; Vasconcelos, 2015). Os principais resultados consistem na identificação de dois riscos potenciais para a compreensão do tema: (a) a aglutinação dos conceitos, o que prejudica avanços teóricos e tratamentos específicos no trabalho; e (b) o isolamento dos tipos em silos, o que contribui para a perda de avanços alcançados em outros domínios. Ainda, postula-se a necessidade de vislumbrar as diversas tipologias de violências em um continuum conforme o contexto social.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".