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Record W4383893303 · doi:10.36510/learnland.v16i1.1096

Meaning and Making: Laying the Groundwork for Community-Based Research-Creation

2023· article· en· W4383893303 on OpenAlexaffvenue
David LeRue

Bibliographic record

VenueLEARNing Landscapes · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsConcordia University
Fundersnot available
KeywordsRealmThe artsMeaning (existential)SociologyMeaning-makingEngineering ethicsPublic relationsPedagogyVisual artsPsychologyPolitical scienceEngineeringArt

Abstract

fetched live from OpenAlex

Research-creation practices have long consulted the public in the process of research, yet the act of making often rests in the hands of the individual researcher. This paper proposes a more integrated and collaborative framework for arts-based researchers and educators called Community-Based Research-Creation, which extends the collaborative logic of oral history into the realm of creation by encouraging art educators to develop focused and prolonged workshops and classes with community. I draw from my own practice as a community art teacher working primarily with adults and propose methods and frameworks for developing community-engaged studies using artworks.

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)0.117
metaresearch head score (Gemma)0.080
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.618

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.080
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.004
Science and technology studies0.0270.212
Scholarly communication0.0450.058
Open science0.0060.036
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0060.002

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 head0.720
GPT teacher head0.638
Teacher spread0.081 · 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
Published2023
Admission routes2
Has abstractyes

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