From Liminality to Transformation: Creating an Art Therapist Identity Through Myths, Metaphors, and Self-Portraits (De la liminalité à la transformation : création de l’identité de l’art-thérapeute à travers mythes, métaphores et autoportraits)
Bibliographic record
Abstract
This paper addresses a gap in the literature on art therapy identity by providing an arts-based autoethnographic account of the author’s identity and growth-related experiences while training as an art therapist and developing a private practice. The data analyzed included personal journaling (written and art-based), course papers, and art made over a six-year period. Using organic inquiry, narrative presentation, and qualitative thematic coding, analyses of the writing and art revealed evidence for the macrolevel themes of liminality (feeling between identities) and transformation (experiencing transformative growth and self/identity integration). Within those macrolevel themes, the art and writing were related to expressive metaphors. For the period of liminality, the metaphors included: (a) hanging between worlds; (b) seeing without sight; (c) shapeshifting; and (d) dark night of the soul. Within the period of transformation, they included moving from: (a) darkness to light; (b) dismemberment to “rememberment”; and (c) death to rebirth. Throughout the narrative, the roles of identity processes (e.g., contemplation), myths, metaphors, and art-making, especially self-portraiture, are discussed as important tools for self-identity development during and after art therapy training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".