Blogging to Counter Epistemic Injustice: Trans disabled digital micro-resistance
Bibliographic record
Abstract
As part of a larger research project on the intersections between transness, disability, cisgenderism (also called transphobia), and ableism/sanism, this article presents the results of a three-month netnography of blog posts made between 2013 and 2019 by selected Tumblr and off-Tumblr blogs authored by people identifying as both trans and disabled. Mobilizing a theoretical framework that combines trans and disability/crip/Mad theory and the notion of epistemic injustice, we highlight the unique possibilities of community building, connection, identity formation, and micro-practices of resistance in trans disabled digital communities. Trans disabled bloggers counter epistemic injustice by speaking back, reclaiming space, and responding to the cisgenderist and ableist/sanist micro-aggressions they experience in their daily lives. This exploration of trans disabled bloggers' micro-activism is divided into four parts. After reviewing the literature in the emerging field of trans disability studies in the first part, the second and third parts present our theoretical and methodological frameworks. Findings are presented and discussed in the three subsections of the fourth part, which delves deeper into our typology of three interrelated genres of trans disabled blog posts: informational, testimonial, and activist. As their names suggest, these genres aim respectively to: 1) inform other trans disabled internet users of identificatory possibilities; 2) testify about bloggers' lived experiences; and 3) advocate for trans disabled people through appeals to users both within and outside trans and disabled communities.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".