Social media as moral laboratory: street involved youth, death and grief
Bibliographic record
Abstract
Street involved youth experience both increased rates of mortality and the all too frequent deaths of people they know, rely on, and care about. In this paper, we explore how street-involved youth dealing with the death of peers or family are engaging in what Mattingly calls ‘moral laboratories’, that is, ‘experiments in how life might or should be lived’ (2014, 27). Drawing from interviews with youth in Victoria, Canada, we analyse aspects of their narratives on social media and grief – finding out about the death, what to post, and supporting others. The affordances of social media mean news of a death can spread like wildfire, private lives become public, the deceased’s reputation is scrutinized and judged, as are the words and actions of youth trying to survive. In offline and online spaces, marginalized youth experiment with expressing their grief, rage, and hope, with mourning and memorializing, navigating fractured and complicated relationships, and finding ways to support themselves and others. We argue that thinking about the ‘moral lives’ of street youth can highlight important aspects of their relationships of family, friendships, and community and their strategies for both protecting these key relationships and finding reasons to keep on living.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".