Expanding on the multidisciplinary stakeholder framework to minimize harms for problematic risk-taking involving emerging technologies. •
Bibliographic record
Abstract
As new types of problematic behaviors and new forms of online risk-taking emerge, forming collaborative relationships while understanding complexities of motivations may help to promote harm reduction and intervention. While it may be too early to form a stakeholder framework without first conceptually understanding the problematic behaviors involved, we attempt to build upon a proposed multidisciplinary stakeholder framework to minimize harms for problematic risk-taking involving emerging technologies. We propose an expansion of roles for individual stakeholders and an expansion of proposed roles for family stakeholders to include partner/spouses, others living in the household, and/or those with close relationships with individuals who are experiencing problems. Empowering individuals who use emerging technologies through participatory action research and knowledge translation/dissemination may lead to improvements in the quality of research and a greater impact on policy and practice. Also, we discuss benefits of industry self-regulation and collaboration on data-sharing practices. We recommend approaches to promote global collaboration with a larger group of relevant stakeholders (including but not limited to individual consumers of technology, families, communities, treatment and welfare providers, researchers, industries, and governments) to address protection of vulnerable populations and reduce harms for users of rapidly advancing technologies.
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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 | 0.066 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".