DIGITAL CULTURES OF CARE, SAFETY AND WELLBEING
Bibliographic record
Abstract
Practices of self-care and social support have long been identified across social media platforms and apps, as people find new ways of using and adapting digital technologies to mediate and address personal and public health issues. But digital health participation is considerably contested and unevenly experienced, whether through the commodified ‘platformization’ of the health sector, or in the potentially ‘unhealthy’ engagement with dominant social media platforms or dating and hookup apps. Contemporary policy frameworks for participatory, digital-enabled healthcare (e.g., NHS, 2019) assume that we all engage in health or help seeking practices online, but have no answers to associated risks of over-exposure, invasive health surveillance or experiences of discrimination and harassment online, particularly for those at the margins. In our case studies, this is pertinent for transgender, non-binary and female hookup app users, people seeking support for mental ill-health, illicit drug users participating in crypto-markets and dark web communities. In response to this scenario, this panel asks: what are the forms and capacities for collective care in the current digital ecosystem, between social media platforms and dating apps struggling to address harassment or mental wellbeing, within health service-supported online forums, and across the dark web? This panel looks at evidence and answers, as well as research practices and ethics, to understand personal and collective attempts to negotiate, manage, circumvent and otherwise find ways to reinvent cultures of care through digital platforms.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".