Expression in the Virtual Public: Social Justice Considerations in Harvesting Youth Online Discussions for Research Purposes
Bibliographic record
Abstract
Information posted by youth in online social media contexts is regularly accessed, downloaded, integrated, and analyzed by academic researchers. The practice raises significant social justice considerations for researchers including issues of representation and equitable distribution of risks and benefits. Use of this type of data for research purposes helps to ensure representation in research of the voices of (sometimes marginalized) youth who participate in these online contexts, at times discussing issues that are also under-represented. At the same time, youth whose data are harvested are subject (often without notice or consent) to the risks associated with this research, while receiving little if any direct benefit from the work. These risks include the potential loss of online social community as well as threats to participant rights and wellbeing. This paper explores the tension between the social justice benefit of representation and considerations that would suggest caution, the latter including inequitable distribution of research-related costs and benefits, and the traditional ethics concerns of participant autonomy and privacy in the context of youth participation in online discussions. In the final section, we propose guidelines and considerations for the conduct of online social media research to assist researchers to balance and respect representational and participant rights or wellbeing considerations, especially with youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".