Relationships, Trust and Accountability, and Critical Reflexivity: Developing Quality Data with the “Hard to Reach”
Bibliographic record
Abstract
Understanding the realities of members of marginalized communities is central to the advancement of social and behavioral sciences. We argue that the development of data accurately representing the perspectives and experiences of such communities is fundamentally contingent on relationships of trust and accountability, and on researcher critical reflexivity. We showcase our methodology in three vignettes based on how we conduct research with communities that are "hard to reach" due to their societal marginalization. These vignettes include our reflections on the quality of data as a function of relationships, trust, accountability, and critical reflexivity. Our stories from the field highlight the importance of relational research with communities experiencing marginalization.
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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 | 0.341 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.081 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".