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Record W2070687917 · doi:10.1075/gest.10.2-3.10ger

Mathematical learning and gesture

2010· article· en· W2070687917 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.

Bibliographic record

VenueGesture · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGestureSophisticationSalientCognitive reframingGraphPsychologyCognitionSightCognitive psychologyComputer scienceArtificial intelligenceSocial psychologyTheoretical computer scienceSociologyNeuroscience

Abstract

fetched live from OpenAlex

This paper reports on a research project in mathematics education involving the use of gesture, movement and vocal sound to highlight mathematically salient features of the graphs of polynomial functions. Empirical observations of students’ spontaneous gesture types when enacting elicited gestures of these graphs reveal a number of useful binaries (proximal/distal, being the graph/seeing the graph, within sight/within reach). These binaries inform an analysis of videotaped gestural and interview data and appear to predict teachers’ assessments of student mathematical engagement and understanding with great accuracy. Reframing this data in terms of C-VPT and O-VPT adds a further layer of sophistication to the analysis and connects it with deeper findings in cognitive and neuroscience and gesture studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.321
Teacher spread0.312 · 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