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Record W1527215696 · doi:10.1002/crq.21086

What Moves Us: Dance and Neuroscience Implications for Conflict Approaches

2013· article· en· W1527215696 on OpenAlexaff
Emily Beausoleil, Michelle LeBaron

Bibliographic record

VenueConflict Resolution Quarterly · 2013
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmbodied cognitionDanceEmpathyGrassrootsPsychologyCognitive scienceCognitionResource (disambiguation)Field (mathematics)SociologyCognitive psychologyEpistemologyNeuroscienceSocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Despite its worldwide use in grassroots conflict approaches, dance, and the body more generally, remain largely unaddressed within conflict theory and conventional practice. We argue that the body is an essential focus of conflict theory and a ready resource for conflict practice by exploring the implications of compelling discoveries within the field of neuroscience. Examining the embodied dimensions of cognition, emotion, and memory, the physical roots of empathy, and the relationship of right‐ and left‐brain processes to conflict, we outline neuroscientific underpinnings of dance‐based approaches to conflict and the range of creative tools that arises from its use.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.020
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.133
GPT teacher head0.332
Teacher spread0.200 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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