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Record W2326485279 · doi:10.1386/atr.2.1.61_1

Explorations with affective engagement: Combining process drama with the Canadian history curriculum

2014· article· en· W2326485279 on OpenAlexaffabout
Jennifer K.M. Cogswell, Debra McLauchlan

Bibliographic record

VenueApplied Theatre Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsBrock University
Fundersnot available
KeywordsDramaFacilitatorProcess (computing)PsychologyCurriculumRelevance (law)Expression (computer science)PedagogySocial psychologyVisual artsArtComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract Teenagers frequently view history as a boring subject, with little relevance to their personal lives. This study integrated historical content and process drama activities to engage teenagers in the topic of World War I. Participants had registered in an after-school drama program offered by an Ontario theatre company. The study aimed to discover whether or not process drama enhanced affective engagement in an historical event and, if so, what specific aspects of drama promoted this engagement. Participant journals and audio-taped interviews, program facilitator observations and researcher field notes provided core data for the investigation. Results indicated that participants did experience affective engagement during the eight process drama sessions. Data analysis uncovered themes related to three major topics: content, response, and creation and expression of ideas. The research yielded seven features of process drama that encouraged affective engagement.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.848
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.138
GPT teacher head0.394
Teacher spread0.256 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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