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Record W4400792103 · doi:10.3389/fcogn.2024.1385087

This time with feeling: recommendations for full-bodied reporting of research on dance

2024· article· en· W4400792103 on OpenAlexaff
Rebecca Barnstaple, Jessie Laurita-Spanglet, Jason Fanning, Christina Tsoules Soriano, Christina E. Hugenschmidt

Bibliographic record

VenueFrontiers in Cognition · 2024
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsUniversity of GuelphYork University
Fundersnot available
KeywordsDanceThe artsPsychological interventionFeelingModalitiesFidelityPsychologyPerspective (graphical)PopulationEmbodied cognitionStyle (visual arts)Engineering ethicsApplied psychologyMedical educationSociologyComputer scienceMedicineSocial psychologyEngineeringVisual artsSocial science

Abstract

fetched live from OpenAlex

Collaborations that employ methods from arts and sciences to address research questions through multimodal study design are becoming more frequent, as it is increasingly apparent that complex challenges require transdisciplinary solutions. These different modalities rely on interdisciplinary exchange while involving unique expertise in associated delivery practices. In human subject driven scientific research specifically, guidelines for arts-based interventions deserve detailed reporting to allow for fidelity, replicability, and uptake of innovation and results. Details such as frequency, duration, delivery method, expected outcomes, historical precedence, and instructor training are crucial, along with nuanced descriptions pertaining to embodied aspects of specific dance or movement style(s) and adaptations made for the population or study design. This Perspective Paper outlines the current state and challenges of reporting on dance interventions and makes recommendations based on our experience as teaching artists who work in research settings alongside researchers who collaborate with dance professionals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.151
GPT teacher head0.431
Teacher spread0.280 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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