The Problem of `Authentic Experience': Storytelling in Anti-Racist and Anti-Homophobic Education
Bibliographic record
Abstract
Educational workshops are a common approach to addressing racism and homophobia in institutional and organizational settings. One underlying rationale of these efforts is that more knowledge of “the other” — non-white and queer participants — will lead to greater equity. This article investigates this premise through empirical research into anti-racist and anti-homophobic workshops in a variety of settings. In particular, our analysis focuses on the uses of “storytelling” and other workshop strategies commonly employed to encourage the disclosure of personal stories by and about the “other.” We argue that, particularly in anti-racist contexts, these strategies have exacted a heavy toll on the tellers, reinforced the exclusionary notions of identity that underlie a racist culture, and had only a limited effect in fostering organizational change. While many of these same problems are present in anti-homophobia educational workshops, differences in the relations of power between the “tellers” and the “listeners,” between who is solicited to tell stories and how, and in the nature of sexual versus racial identity also suggest important distinctions between these different forms of educational practice. Within this context however, queer youth of color and transgender youth continue to report that storytelling can exact a heavier toll on them than others. There is little empirical literature analyzing anti-oppression workshops and their effects in both schools and community organizations. Our study addresses this gap in the sociological literature.
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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 | 0.036 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".