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Record W2952252457 · doi:10.1371/journal.pone.0218179

Assessing the reliability of the Laban Movement Analysis system

2019· article· en· W2952252457 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaIndustry CanadaCanarie
KeywordsAnnotationComputer scienceReliability (semiconductor)SalientGraphMovement (music)Artificial intelligenceGestureTheoretical computer science

Abstract

fetched live from OpenAlex

The Laban Movement Analysis system (LMA) is a widely used system for the description of human movement. Here we present results of an empirical analysis of the reliability of the LMA system. Firstly, we developed a directed graph-based representation for the formalization of LMA. Secondly, we implemented a custom video annotation tool for stimulus presentation and annotation of the formalized LMA. Using these two elements, we conducted an experimental assessment of LMA reliability. In the experimental assessment of the reliability, experts-Certified Movement Analysts (CMA)-were tasked with identifying the differences between a "neutral" movement and the same movement executed with a specific variation in one of the dimensions of the LMA parameter space. The videos represented variations on the pantomimed movement of knocking at a door or giving directions. To be as close as possible to the annotation practice of CMAs, participants were given full control over the number of times and order in which they viewed the videos. The LMA annotation was captured by means of the video annotation tool that guided the participants through the LMA graph by asking them multiple-choice questions at each node. Participants were asked to first annotate the most salient difference (round 1), and then the second most salient one (round 2) between a neutral and gesture and the variation. To quantify the overall reliability of LMA, we computed Krippendorff's α. The quantitative data shows that the reliability, depending on how the two rounds are integrated, ranges between a weak and an acceptable reliability of LMA. The analysis of viewing behavior showed that, despite relatively large differences at the inter-individual level, there is no simple relationship between viewing behavior and individual performance (quantified as the level of agreement of the individual with the dominant rating). This research advances the state of the art in formalizing and implementing a reliability measure for the Laban Movement Analysis system. The experimental study we conducted allows identifying some of the strengths and weaknesses of the widely used movement coding system. Additionally, we have gained useful insights into the assessment procedure itself.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.062
GPT teacher head0.302
Teacher spread0.239 · 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