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Record W4283024585 · doi:10.1177/15327086221098200

Direct[Message]: Exploring Access and Engagement With the Arts Through Digital Technology in COVID Times

2022· article· en· W4283024585 on OpenAlexafffund
Tara La Rose, Carla Rice, Carmela Alfaro-Laganse, Colina Maxwell, Rana El Kadi, Christina Luzius-Vanin, Michele Fisher, Simon Lebrun, J. Ruxton, David Bobier, Cathy Paton, Suad Badri, Sheila O’Reily, Maggie Perquin, Kathy Smith, Kusum Bhatta, Becky Katz

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsOntario College of Art and DesignUniversity of GuelphMcMaster University
FundersCanada Council for the Arts
KeywordsThe artsPandemicCoronavirus disease 2019 (COVID-19)Isolation (microbiology)Process (computing)Social isolationCommunity engagementPublic relationsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakSociologyPolitical scienceComputer sciencePsychologyMedicine

Abstract

fetched live from OpenAlex

Direct[Message], a community-based research (CBR) project developing a digital platform supporting older adults engagement with the arts through digital technologies, faced the challenge of redesigning the research protocol after the COVID-19 pandemic was declared in March 2020. The redesign, which brought challenges and opportunities, allowed the research team to embed the project with process goals considering older adults’ experiences of social isolation, and exploring how these experiences might be mitigated by greater access to the arts through technology. This article explores the redesign process undertaken by the Direct[Message] team and presents preliminary findings from this multiyear study.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.004
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.255
GPT teacher head0.458
Teacher spread0.203 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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Same venueCulture Studies &#x2194 Critical MethodologiesSame topicTechnology Use by Older AdultsFrench-language works237,207