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Record W7064654843

Creating Learning Communities: an analysis of public events at the Art Gallery of Ontario and the Toronto Biennial of Art

2020· other· en· W7064654843 on OpenAlexaboutno aff

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2020
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsArt galleryAgency (philosophy)Context (archaeology)ColonialismInstitutionThe artsRelation (database)ExhibitionEvent (particle physics)
DOInot available

Abstract

fetched live from OpenAlex

This major research paper (MRP) analyzes the potential of bell hooks’s notion of learning communities within the context of contemporary arts institutions in the city of Toronto. It considers how two public programs—the roundtable discussion "Ways of Caring" at the Art Gallery of Ontario and the public gathering by BUSH gallery, "Beach(fire) Blanket Bingo Biennial", presented by the Toronto Biennial of Art—created learning environments that engaged participants in critical thinking, dialogue and self-reflexive practice. In doing so, each event challenged the colonial impositions and constructs of the host institution while subverting the structures that exclude racialized communities from their narratives. The MRP examines the diverse means through which learning communities take form, following three categories of analysis: ritualistic impositions, as discussed by Carol Duncan; participation and collective agency in relation to the writings of Claire Bishop, Pablo Helguera, and Irit Rogoff; and lastly, learning communities, as articulated by bell hooks. The essay ultimately seeks to prove that, by engaging in radical pedagogical approaches, museum education and discursive programs can challenge the institution’s colonial histories and structures by prioritizing and amplifying the voices of BIPOC communities.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.927
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.039
GPT teacher head0.274
Teacher spread0.236 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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