Two Eyes Seeing: Transforming the Narrative of a Persian Art Therapy Student Through Art, Language, and Traditional Knowledge
Bibliographic record
Abstract
This art-based research paper seeks to report on the researcher’s responses to the following questions: How can a culturally sensitive art therapy process provide a space for an Iranian art therapy student to connect with her inherited cultural wisdom and use the knowledge of her ancestors to rewrite her own narrative? And how might this process contribute to a positive configuration of hybrid and diasporic identity in relation to different categories of ethnic, social, and professional identities? This research addresses the necessity of incorporating culturally sensitive materials in the healing process of ethnic minorities; in this case, literature and poetry for Iranian diaspora who carry a collective trauma and facing ethnic and identity crises after displacement in Canada. The writer’s personal creative exploration of ethnic identity when her social identity was weakened led to the creation of a diasporic and hybrid identity. The findings of this research could be of interest to therapists and mental health professionals working with Iranian diaspora who would like to increase their awareness of the acculturation process and the importance of building cultural understanding regarding the social context and associated identity crisis among Iranian diaspora from collective trauma and learn how traditional knowledge of healing could help recreate a new narrative.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".