Taking Social Justice to a Different Stage
Bibliographic record
Abstract
Knowledge creation through art has the potential to serve as an emancipatory approach in health research, education, and practice by promoting connection and dialogue; challenging dominant paradigms of knowledge; and legitimizing, empowering, and promoting traditionally marginalized voices. Poetry, as one art form, may be an effective method for promoting reflexivity, critical thinking, empathy, and a heightened understanding of social justice issues among students and professionals. This research explored poetry as a means of advancing health equity and social justice through the feedback shared by a group of participants who attended a poetry workshop titled, “Taking Social Justice to a Different Stage: How Poetry Promotes Emancipatory Health Narratives”. The data consists of quantitative and qualitative responses from pre- and post- workshop surveys. The quantitative results indicate that after the workshop, participants were less likely to believe that poetry should only be used to entertain, and were more likely to believe that poetry is a powerful method for promoting health equity. The qualitative analysis reveals multiple themes in participant responses from the post-workshop survey: 1) empowerment; 2) connection and perspective sharing; and 3) social justice promotion through arts-based methods. These results indicate that poetry may promote different forms of knowing, foster emotional connection and perspective sharing, and create more awareness about health inequities and social justice issues. Hence, poetry may be a valuable addition to health care research and education, and the promotion of social justice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".