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Record W2898433385 · doi:10.36510/learnland.v10i1.726

Building an Urban Arts Partnership Between School, Community-Based Artists, and University

2016· article· en· W2898433385 on OpenAlexaffvenueabout
Bronwen Low, Mindy R. Carter, Elizabeth Wood, Claudia Mitchell, Melissa Proietti, Debora Friedmann

Bibliographic record

VenueLEARNing Landscapes · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsMcGill UniversityCommunity Based Research Centre
Fundersnot available
KeywordsGeneral partnershipThe artsCurriculumVisual arts educationSociologyEmpowermentService-learningPlan (archaeology)PedagogyArts in educationVisual artsPolitical scienceArtGeography

Abstract

fetched live from OpenAlex

This paper explores a partnership between a high school, university researchers, and community artists in the service of improved student learning, empowerment, and self-expression through the urban arts. The Urban Arts Project partners teachers at James Lyng high school with hip-hop and other urban artists to develop units across the curriculum, supported by subject-area specialists from McGill’s Faculty of Education. In this article, we introduce the project and what we have learned about processes of school reform through cross-sectoral collaboration from the rst year of our partnership. This includes sharing the perspectives of the teachers and artists most actively involved in the rst year’s initiatives (with video interview links).

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)0.020
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 score0.024
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0240.011
Scholarly communication0.0120.012
Open science0.0030.038
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0140.002

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 head0.049
GPT teacher head0.260
Teacher spread0.211 · 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

Citations4
Published2016
Admission routes3
Has abstractyes

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