Crip Encounters in the Corporate Hashtag: Complicating #BellLetsTalk
Bibliographic record
Abstract
This article investigages the annual #BellLetsTalk mental health awareness campaign through the lens of critical disability studies. Created by the telecommunications company Bell Canada, the campaign encourages social media users to share, promote, and like posts about Bell, mental health, and hyperindividualist narratives of overcoming disability and illness. However, delving deeper into the archive uncovers dissenting crip voices that resist Bell's hegemonic narrative of wellness and cure. Focalizing my analysis around a sample of 5000 tweets (including 2093 unique tweets) from the 2018 campaign, I identify distinct disabled networks emerging around the hashtag on Twitter/X: 1) feminine therapeutic networks, 2) masculine sick publics that retain an attachment to capitalism and nationalism, and 3) queer communities that keep company with ghosts. Furthermore, I identify users deliberately disidentifying with the network and occupying the role of the killjoy or "bad" avatar. This article articulates an ethical, hybrid qualitative and quantative method for working with data.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".