Unraveling my Constructed Socio-Cultural Identity: A Heuristic Arts-Based Critical Self-Inquiry
Bibliographic record
Abstract
In the field of art therapy, scholars with a critical lens have criticized the reductionist shortcomings of the multicultural competence trend. They argue for the need to integrate an intersectional framework and social justice approach in art therapy that acknowledges structural power and systemic oppression and urges art therapists to undergo critical self-reflexivity. In line with this approach, and through a postcolonial feminist lens, the researcher investigates her sociocultural identity. Starting at a cognitive level and then digging into her body, mind and soul through artmaking, imaginal dialogue, and writing. This research utilized a heuristic-arts-based methodology to address the researcher's own journey in critical self-examination of her different identity markers shaped by interconnected systems of oppression. The researcher was guided by Moustakas' (1990) six-step heuristic inquiry and used various approaches and forms of art throughout the research process. The main themes deciphered are discussed in this contextual essay and portrayed in the video performance: Unraveling and Reconciling Fragments of Myself. Link: https://nataliortiz77.wixsite.com/fragments
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".