Stim, Like, and Subscribe: Autistic Children and Family YouTube Channels (Dispatch)
Bibliographic record
Abstract
The title of this dispatch comes from a play on words of the ubiquitous YouTuber call-out, typically uttered within the first 30 seconds of a video after a cheery "Hey guys!." "Likes" or "thumbs up" bump up the position of a video in the site's algorithm and allow YouTube to fine-tune recommendations by showing users videos that others who have similar interests spend their time watching (Arthurs et al., 2018).This dispatch examines the challenges posed by the family vlogging channel Fathering Autism (FatheringAutism, n.d.), which focuses on disability and family dynamics and bills itself as a distinctive voice in the digital disability space.I aim to question the oft-muddied distinction between education and entertainment in Fathering Autism's content, in order to contribute to discussions surrounding social justice and Autism.Specifically, I question whether it is ethical to create content that relies heavily on the participation of a nonspeaking autistic child.This question is part of a larger debate that considers whether it's ethical to cast any children in homegrown content, given the limits to the consent they can give to being filmed.Asa Maas, the vlogger behind the Fathering Autism channel, has credited his family's success on the platform to their overlapping circles of identity.Maas put it succinctly when he accepted an award at a Healthcare Disruptors Industry conference in November of 2019: not only is Fathering Autism a family vlogging channel, it is also an Autism family channel, as well as one that focuses on an autistic girl.A recent systematic review and meta-analysis pegged the ratio of Autism in boys and girls at 3:1, which is higher than the research community's previously stated and widely circulated benchmark of 4:1 (Loomes et al., 2017).This means that families like the Maas' are underrepresented in depictions of Autism, which are only slowly becoming
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.002 |
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".