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Record W2727189850 · doi:10.1080/23311983.2017.1351704

Audience responses to<i>Contact!Unload</i>: A Canadian research-based play about returning military veterans

2017· article· en· W2727189850 on OpenAlexafffundabout
George Belliveau, Jennica Nichols

Bibliographic record

VenueCogent Arts and Humanities · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsFocus groupPerceptionAudience responsePsychologyTarget audienceSociologyPublic relationsPolitical scienceAdvertisingEngineering

Abstract

fetched live from OpenAlex

Contact!Unload, a research-based play co-developed with veterans and community members, depicts the experiences of a group of veterans serving in Afghanistan (and elsewhere) and their transition home after overseas combat. The play was first produced in April 2015 in a professional theatre venue in Vancouver, and has subsequently been staged 15 times across Canada. To date, eight veterans have taken part as performers in this theatre initiative led by researchers in counselling psychology and theatre. This article takes a close look at the impact the theatre project has had on audience members and their perceptions of the play. Audience impact was measured through a mixed methods approach, using three focus group sessions, four interviews and a post-production written survey by audience members.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.326
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0220.007
Scholarly communication0.0050.001
Open science0.0020.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.227
GPT teacher head0.360
Teacher spread0.133 · 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

Citations16
Published2017
Admission routes3
Has abstractyes

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