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Record W2948519276 · doi:10.4324/9781315144245-19

Using arts to engage community

2018· book-chapter· en· W2948519276 on OpenAlexaboutno aff
Marleny M. Bonnycastle, Tuula Heinonen

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsSociologyVisual artsPsychologyArt

Abstract

fetched live from OpenAlex

In this chapter, the authors share them stories and experiences of integrating arts into social work education in two different contexts: Nepal and Canada. They discuss how their used arts in two different settings: with groups of people affected by physical and social–environmental issues, such as natural disasters and armed conflicts, and in the other, with students learning about practice and inquiry in social work. In both settings, arts-based methods were used as pedagogic strategies to engage students in critical thinking, learning social work values and theory, as well as developing skills for their future professional practice. The main purpose of the art workshop was to increase participants&s; capacities to provide post-earthquake responses and engage their community using community engagement approaches such as arts-based activities. The authors use creative activities that focus on how children might create visual representations and find their own words to name their feelings, putting some distance between them and their feelings.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0080.005
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0240.004

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 head0.394
GPT teacher head0.351
Teacher spread0.043 · 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 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
Published2018
Admission routes1
Has abstractyes

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