Sharing Data and Results in Ethnographic Research: Why This Should Not Be an Ethical Imperative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RESEARCHERS RECENTLY HAVE argued that offering to share research results with study participants should be an "ethical imperative." This article considers that suggestion in light of the practice of ethnographic, particularly anthropological, research. Sharing results is discussed in relation to several issues, e.g., whether it occurs during or after completion of a project, whether the research is long-term, the complexities involved in depositing field materials in archives, the changing politics of ethnographic research, research not concerned with communities, situations in which participants and the anthropologist may be in danger, and changing styles of ethnographic research. I argue that, ideally, sharing should be a regular component of ethnographic research but should not be an ethical requirement. Given the complexity, variety and changing political contexts of ethnographic research, implementing such a requirement would often be practically impossible and sometimes would be inadvisable. I recommend instead that research ethics boards educate themselves about the nature of ethnographic research. Further, they should approach decision making on the issue of data or results sharing on a case-by-case basis. For researchers, I recommend that discussion of data and result sharing should become part of the education of all ethnographers and that discussion of the issue should be fostered.
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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 | MetaresearchOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchOpen scienceScholarly communication Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.827 | 0.530 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.101 |
| 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 it