Expressing, Communicating and Discussing Suicide: Nature, Effects and Methods of Interacting through Online Discussion Platforms
Bibliographic record
Abstract
In many countries, the Internet is a source frequently used to look up health information (Fox & Duggan, 2013; McDaid &Park, 2010). Several surveys conducted in the United States have focused on the increasingly important role that social media plays in providing access to health information (Health Research Institute, 2012; The Change Foundation, 2011a; Chou et al., 2009; Fox & Jones, 2009). Social media platforms are especially popular among teens and young adults researching sensitive topics such as sexuality, mental health, drug use including recreational use of prescription medication, and se If-harm (Buhi et al., 2009; Gray et al., 2005; Gray & Klein, 2006; Harvey et al, 2007; Tackett-Gibson, 2007, 2008; Thoër & Aumond, 2010; Aube & Thoër, 2009; Whitlock, Powers & Eckenrode, 2006). On these platforms, individuals are exposed to information from numerous sources, including governments, health institutions, health professionals, private health service providers and, most importantly, peers and family members (Romeyer, 2008).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".