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Record W4390104897 · doi:10.33524/cjar.v22i3.589

Social Portraiture: Decolonizing Ways of Knowing in Education through Arts-Based Participatory Action Research

2022· article· en· W4390104897 on OpenAlex
Peggy O'Neil, Roula Kteily-Hawa, Marlene Janzen Le Ber

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Action Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningParticipatory action researchSociologyThe artsAction researchCitizen journalismAction (physics)PedagogyDemocracySocial changeVisual artsPoliticsPolitical scienceArt

Abstract

fetched live from OpenAlex

As scholars work to decolonize educational research, new methodologies are needed. In this paper, we present our conceptual premises for a new paradigm, social portraiture, which combines participatory action research (Freire, 1982) and portraiture (Lawrence-Lightfoot & Hoffmann, 1997), and extends to include archival records and social art. This democratic approach integrates student participation, community engagement, and social change, coalescing into an integrated portrait of human experience in education. Our ideas contribute to foundations in arts-based methods in participatory action research, seeking transformative change in educational world views, ways of knowing, and institutional ways of life.

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)0.068
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0680.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.945
GPT teacher head0.737
Teacher spread0.208 · 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