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Record W2992224963 · doi:10.16997/jdd.298

Me on the Map: A Case Study of Interactive Theatre and Public Participation

2018· article· en· W2992224963 on OpenAlexaffabout
Stephen Williams, Jan Derbyshire, Adrienne Wong

Bibliographic record

VenueJournal of Deliberative Democracy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDeliberationCitizen journalismFoundation (evidence)HappinessCommissionParticipatory designPublic participationPublic relationsSociologyPolitical sciencePsychologyEngineeringSocial psychologyLawOperations management

Abstract

fetched live from OpenAlex

Me on the Map (MOTM) is a unique participatory show for classroom-sized groups of young people aged 6-15. Initially developed and produced by Neworld Theatre in Vancouver, through a commission from the Vancouver International Children’s Festival, MOTM challenges participants to collectively solve the problem of how to best develop an actual lot of land that sits empty in their city. The MOTM experience guides participants through co-design activities that start in the classroom. The choice students make provide data that forms the foundation for the decisions made during the performance. This paper details the theoretical background of the show including participatory theatre, inclusive design, urban happiness studies and ethical decision making. We present lessons learned and make recommendations for public deliberation practitioners on using this technique in future projects.

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)0.011
metaresearch head score (Gemma)0.021
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 score0.031
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0310.016
Scholarly communication0.0090.007
Open science0.0040.011
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.471
GPT teacher head0.608
Teacher spread0.137 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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