#DIYDiabetes: Type One Diabetes, Stigma, and Control in Digital Networks of DIY Looping
Bibliographic record
Abstract
The general public’s awareness of type one diabetes is often lacking and filled with misinformation that can lead to stigma around the disability. Often type one diabetics have to fend for themselves through the means of “DIY diabetes.” The delay in technological innovations and the slow approval rate of Health Canada, compared to the US Food and Drug Administration, makes improving the lives of those with diabetes much more challenging. The self-management of the disability enables diabetics to take agency of their treatment plan and is the primary reason for DIY diabetes. The main reason for this research is to bring awareness to the praxis of disability studies and diabetes representation in academic literature. There is a gap between understanding the theory of disability and recognizing disability praxis in popular culture. A central problem to this gap is the disparities between the diabetes community and Canadian and US institutions in self-management. Here I provide a technography, or a study of how technology functions in society, on DIY Loop and other DIY diabetes in North America. I show that, despite government reluctance to approve medical devices for disabilities, DIY diabetes is improving diabetes management and how the network of the diabetes community evolves with its ingenuity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".