Difference-attuned witnessing: Risks and potentialities of arts-based research
Bibliographic record
Abstract
In this paper, we interrogate notions of affect, vulnerability and difference-attuned empathy, and how they relate to bearing witness across difference—specifically, connecting through creativity, experiencing the risks and rewards of vulnerability, and witnessing the expression of difficult emotions and the recounting of affect-imbued events within an arts-based process called digital/multi-media storytelling (DST). Data for this paper consists of 63 process-oriented interviews conducted before and after participants engaged with DST in a research project focused on interrogating negative concepts of disability that create barriers to healthcare. These retrospective reflections on DST coalesce around experiences of vulnerability, relationality, and the risks associated with witnessing one’s own and others’ selective disclosures of difficult emotions and affect-laden aspects of experiences of difference. Through analysing findings from our process-oriented interviews, we offer a framework for understanding witnessing as a necessarily affective, difference-attuned act that carries both risk and transformative potential. Our analysis draws on feminist Indigenous (Maracle), Black (Nash) and affect (Ahmed) theories to frame emerging concepts of affective witnessing across difference, difference-attuned empathy, and asymmetrical vulnerability within the arts-based research process.
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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 | 0.098 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.091 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.005 | 0.008 |
| 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".