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Emotions and social conflicts: mobilisations against animal husbandry in Brittany, France

2023· article· en· W4383197660 on OpenAlexaff
Ali Romdhani, Véronique van Tilbeurgh

Bibliographic record

VenueEmotions and Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAction (physics)ReflexivityCollective actionGRASPEpistemologySociologySocial conflictSocial animalFocus (optics)Social psychologyRationalityEnvironmental ethicsPsychologyPolitical scienceSocial scienceComputer scienceLawEcology

Abstract

fetched live from OpenAlex

Social conflicts have been largely studied according to their rational aspect, looking at arguments and strategies mobilised by rationally engaged actors. However, land-use conflicts over animal husbandry in Brittany have shown that a pre-reflexive element comes into play, motivating action before justification. Emotions are a ‘forgotten variable’ of individual and collective action that can be fruitfully grasped in a relational approach, as a component of any social relations. In this article, we propose a theoretical framework to understand the role of emotions in social conflicts. The focus is on situations where people engage in conflict rather than avoid it. We base our framework on Dewey’s account of emotions as a ‘disruption in routine’ and rational thinking as an effort to readjust. To fully grasp the dynamics of conflict, we also suggest that the processes of emotional revision, routine, trust and values are essential components of collective action in situations of conflict.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.237
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.016
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.281
Teacher spread0.241 · 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
Published2023
Admission routes1
Has abstractyes

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