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Record W2025428265 · doi:10.3389/fnhum.2012.00162

The Amazing Capacity to Read Intentions from Movement Kinematics

2012· article· en· W2025428265 on OpenAlexaff
Sukhvinder S. Obhi

Bibliographic record

VenueFrontiers in Human Neuroscience · 2012
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsKinematicsMovement (music)Physical medicine and rehabilitationComputer sciencePsychologyCognitive psychologyCommunicationArtPhysicsMedicineAestheticsClassical mechanics

Abstract

fetched live from OpenAlex

kinematics as competitive or cooperative in contexts where these two behavior types are relevant (e.g., military bases, police stations, airports, or even nightclubs and bars). Based on the output of such classifiers, an individual could be "flagged" as a potential threat, and security personnel could be on guard to respond. This form of classification, although perhaps not viable right now, would solve many social problems relating to issues such as racial profiling. With such a system, the term would be "kinematic profiling" and the kinematics may well be irrespective of race or social class.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.577
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

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

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 head0.082
GPT teacher head0.332
Teacher spread0.250 · 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 designObservational
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

Citations15
Published2012
Admission routes1
Has abstractyes

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