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Record W4225012005 · doi:10.4324/9781003149538-6

Borders and Boundaries in Virtual Art Therapy

2022· book-chapter· en· W4225012005 on OpenAlexaboutno aff
Michelle Winkel

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsCultural humilityCuriosityPsychologyHumilityCultural competenceOpenness to experienceCreativityMulticulturalismPsychotherapistAestheticsSocial psychologyPedagogyArtPolitical science

Abstract

fetched live from OpenAlex

This chapter focuses on the concepts of cultural competence and cultural humility, which help art therapists navigate the diverse cultural landscapes of the client–art therapist relationship in the new global world of virtual art therapy. Cultural competence refers to the knowledge that has been acquired through one’s life experiences and training and that informs people’s social behaviours. Cultural humility, in contrast, is a critical reflection regarding personal and systemic biases, and entails a respectful approach to relationships based on mutuality. Using excerpts from a November 2020 interview with Girija Kaimal, research by the Canadian International Institute of Art Therapy, and case vignettes, this chapter argues that art making and creativity, the core of our work as art therapists, are key resources to help us navigate these cultural divisions and build relationships in a therapeutic context that spans boundaries and crosses borders. The practice of making art compliments the curiosity, openness, and exploration valued in these concepts of cultural humility and competence in a way that spoken language alone does not. Art interventions discussed in this chapter are based on videoconferencing sessions with art therapy clients.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.038
GPT teacher head0.259
Teacher spread0.221 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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