A Critical Dialogue on Ethics and Practices for Digital Research with “Difficult” to Reach Populations
Bibliographic record
Abstract
ABSTRACT The panel examines approaches to working with “difficult”‐to‐reach populations across all stages of the qualitative research process, beginning with project scoping and obtaining the institutional review board approval to member checking with participants and reporting out findings. The panel identifies tension between institutional mandates for research and the ethical commitments scholars make to their participants and those participants’ broader communities. Avoiding a perfect solution to ethical methods of research, this panel instead identifies strategies and tools for ensuring that such research affirms and enhances the lived experiences of these populations, while avoiding extractive and deficit‐based models of scholarly inquiry. The panel utilizes case studies from the respective panelists about their challenges and successes working with “difficult”‐to‐reach populations. The panel includes a group discussion led by the panelists on pending questions of researching with “difficult”‐to‐reach populations. This group discussion will involve an opportunity for members of the audience to ask questions and share their experiences with similar research endeavors. To make the most of the time together, the panelists will also compile resources highlighted within the group discussions and share those resources with attendees during and after the conference.
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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 | 0.280 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.075 |
| Scholarly communication | 0.041 | 0.033 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.028 | 0.048 |
| Insufficient payload (model declined to judge) | 0.005 | 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".