Cultures of the Internet: Identity, community and mental health
Bibliographic record
Abstract
The Internet and World Wide Web have woven together humanity in new ways, creating global communities, new forms of identity and pathology, and new modes of intervention. This issue of Transcultural Psychiatry presents selected papers from the annual McGill Advanced Study Institute (ASI) in Cultural Psychiatry on ‘‘Cultures of the Internet’’ which took place in Montreal, April 26–29, 2011. The ASI addressed four broad areas: (a) how the Internet is transforming human functioning, personhood, and identity through the engagement with electronic media; (b) how electronic networking gives rise to new groups and forms of community, with shifting notions of public and private, local and distant; (c) the emergence of new pathologies of the Internet, e.g., Internet addiction, group suicide, cyberbullying, and disruptions of neurodevelopment; and finally, (d) the use of the Internet in mental health care, for example, by consumer advocacy and support groups, aswell as for the delivery of health information, web-based consultation, treatment intervention, and mental health promotion. In addition to some of the ASI papers, this issue includes other recent contributions to the journal on related themes. In this introductory essay, we set out some of the broad implications of the Internet and related new media and information communication technologies (ICT) for cultural psychiatry.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".