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Record W7071238772

Sites of Learning: Rural Artist Residencies in the East, West, and North of Canada/Turtle Island

2023· dissertation· en· W7071238772 on OpenAlexaffabout

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldArts and Humanities
TopicLinguistic research and analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsNucleofectionPretextGestational periodArticular cartilage damageDemotionLiquation
DOInot available

Abstract

fetched live from OpenAlex

As spaces for professional development and creative incubation, artist residencies represent important stepping stones in artists’ careers. Often overlooked are those established in rural areas, which serve as sites for community gathering, sharing, and learning. Rural communities also face distinct challenges, such as mass urban migration, imminent climate change, and waning support for rural education studies. The purpose of this thesis is to identify what practices are underway at three rural Canadian artist residencies, by examining educational infrastructures, environments, and attitudes. Using case study and autoethnographic methods (written and visual journaling, field notes, interviews) these practices are explored from the perspectives of visiting artists-in-residence, local residents, and residency staff. The results illustrate innovative strategies that seek to empower, connect, and grow artist residencies as places of public pedagogy, contextualized by their rural locales. Described using six themes, they depict learning at rural artist residencies as: (a) embedded in social context, (b) situated in relation to the land, (c) occurring at an intersection of industries, (d) involving informal learning relationships, (e) requiring a hunger for knowledge, and (f) rooted in issues of access. This study suggests that the resulting infrastructural, environmental, and attitudinal practices can be mobilized to sustain and/or revitalize rural communities through dynamic networks, socially engaged programs, and arts advocacy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.252
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes2
Has abstractyes

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