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Record W1774185590 · doi:10.7870/cjcmh-2015-005

Using Art to Tell Stories and Build Safe Spaces: Transforming Academic Research Into Action

2015· article· en· W1774185590 on OpenAlex
Jeff Karabanow, Ted Naylor

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStorytellingParticipatory action researchAction researchCitizen journalismSociologyThe artsSpectacleSpace (punctuation)Field (mathematics)Action (physics)PostmodernismReflection (computer programming)Engineering ethicsPerforming artsProcess (computing)AestheticsVisual artsPolitical sciencePedagogyEpistemologyComputer scienceEngineeringArt

Abstract

fetched live from OpenAlex

This paper explores how art can be used to tell stories and actively build safe spaces, and grew out of reflections from a capacity-building and knowledge translation/mobilization project involving 7 young people living on the streets. The paper considers how research can contribute to an examination of anti-oppressive practice and methodology, and an application of it in the field through an arts-based agenda. Conceptually, the paper takes up the postmodern turn in methodological considerations by exploring how the “spectacle” of a research agenda can come to be undone by a more participatory research process. This paper speaks to the processes involved in creating an arts-based environment and, ultimately, the building of a community space for sharing, for reflection, and for mobilization—storytelling not only as a form of art, but as a critical methodology.

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)0.033
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.920
GPT teacher head0.742
Teacher spread0.178 · 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