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Record W2350900880 · doi:10.14288/1.0098185

Developing a performance with special needs students : a case study in creativity

2010· article· en· W2350900880 on OpenAlexaboutno aff
David Secunda

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityMathematics educationPedagogyPsychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

This thesis advances the proposition that learning disabled students' participation in kinesthetic or dance and mime performance activities provides an alternative expressive mode to the verbally oriented activities through which creativity has traditionally been examined and evaluated. Between February and May, 1989, I designed and carried out research project in an elementary school in Vancouver, B.C. A group of students, characterized as "learning disabled," participated under my direction in the design, rehearsal, and presentation of a performance of mime and movement to a narrated text developed mainly by the students. Techniques of participant observation and interview (as well as videotape recordings) were used to document both students' activities and the responses of students and their teachers The technique of cognitive mapping was used to analyze observations of the students in kinesthetic activities. Results of this case study have implications for theory and practice. Theoretical implications relate to conceptions o creativity derived from Maslow's description of "peak experiences" and from analyzing Weisberg's definition of creativity. An applied outcome of this research allows practical generalizations about the use, design, and implementation of programmed kinesthetic activities as a means of encouraging creativity among learning disabled students.

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)0.005
metaresearch head score (Gemma)0.013
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0190.008
Scholarly communication0.0060.003
Open science0.0040.007
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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