Tensions Between Anonymity and Thick Description When “Studying Up” in Genetics Research
Bibliographic record
Abstract
Anonymity, according to Tilley and Woodthorpe, refers to removing or obscuring participant information, whereas "confidentiality refers to the management of private information." Both are major considerations for ethics review boards, but can be challenges when "studying up" in qualitative research because of the depth, precision, and uniqueness of the information, and the prominence of research participants. In anthropology, providing detailed and nuanced accounts of particular spaces, events, and conditions is essential. Actions taken to hide or gloss over these particulars would impede the ability to demonstrate authenticity, validity, and verisimilitude. As social science moves into field sites such as cutting-edge genomics, where when studying up, participants through their particular contributions might be identified, strategies to decrease the friction between descriptive methodologies and the requirement for anonymity need to be developed. We conclude with recommendations for researchers and members of research ethics boards regarding how to anticipate and mitigate this tension.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.512 | 0.469 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.028 | 0.121 |
| Scholarly communication | 0.023 | 0.042 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".