Sick kids versus whom? Childhood disability and charitable campaigns on Instagram
Bibliographic record
Abstract
Platform media are changing the disability charity landscape. This paper employs a hybrid critical disability studies – platform media studies lens to explore the SickKids VS campaign, aiming to ‘fight’ childhood illness and disability. Employing a social media thematic analysis, we analyzed social media content distributed through the campaign, consisting of images, videos, and captions ( n =620). We found three dominant narratives: heroic sick kids, crumbling infrastructure, and informational content. Each trend, we argue, emerges within a changing platform mediascape, whereby charitable audiences must be cultivated and curated over a long-term process, rather than in a single moment, as in telethon fundraising. We ask how disability is framed in each of those narratives, and how disability studies might respond to these formulations in the political economy of platform media. We end by exploring the strategies disability studies can take to combat the marginalizing effects of such charitable campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".