Bibliographic record
Abstract
Although qualitative research ethics have typically focused on participant risk, there is a growing recognition that researchers themselves face ethical risks, including the emotional impacts of research on sensitive topics. There is still considerable room for discussion about what it means to conduct sensitive research and how researchers are impacted by such work. In my research about nursing work, I encountered unexpected ethical challenges. These situations included hearing and responding to disagreeable participant statements, listening to distressing stories, managing the high expectations of research participants in terms of the purpose and outcomes of the research, and facing my own potential professional marginalization because of the political nature of my research. By extant definitions, my research might not be considered sensitive. As well, I faced consequences beyond emotional harms. Thus, I propose a broadened understanding of sensitivities in research and their consequences in order to acknowledge the potential for researchers’ ethical distress in all qualitative research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.350 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.017 | 0.182 |
| Scholarly communication | 0.025 | 0.044 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.020 | 0.029 |
| 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, 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".