Collaging connection, stitching survival: reflections from an arts-based assignment in a social work trauma course
Bibliographic record
Abstract
Social work programs are increasingly offering trauma education as part of their core and specialized curriculum, but little focus has been given to how students are being taught and evaluated. This co-written article (professor and students) explores the value of assignments that embrace a decolonial approach by offering opportunities for creative exploration, complexity, and self-reflexivity. In particular, we detail one assignment where students create a piece of art in response to an aspect of trauma or way of surviving, coupled with an artistic statement with links to course content. Based on the lack of writing on the topic of arts-based assignments in social work trauma courses, we provide details of the course context, preparing students for the assignment during early classes, assignment instructions, grading, and six student examples of artistic submissions and statement excerpts. The professor and these students then provide reflections on what they learned from the assignment, what helped them in the meaning-making process, and suggestions for the future. The article concludes with areas for future research and pedagogical explorations.
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 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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".