Sensitive Research and the Collision of Advocacy and Research: Consequences for the Researcher
Bibliographic record
Abstract
Doing sensitive research presents particular problems over and above other social science research because of the nature of the issues being asked about and their potential impact on both the participant and the researcher. In some instances, the factors that engage the researcher in the project may also be of interest to advocates who are working for the benefit of the participants in the research. In the current instance, the researcher was interested in understanding the impact of “life without” the possibility of parole on the lives of women convicted of killing their abusers, as well as the impact of their imprisonment and clemency on their families. At the same time, a powerful advocacy group was devoted to gaining clemency for the women. The resulting clash between the goals and purposes of these two entities resulted in a significant impasse and unanticipated consequences for the research agenda as well as the researcher. Analysis of one specific research project and the problems resulting from the clash between researcher and advocates can contribute to the literature on sensitive research as well as on the clashes between advocacy and social science 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 | 0.668 | 0.599 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.057 | 0.340 |
| Scholarly communication | 0.050 | 0.084 |
| Open science | 0.011 | 0.066 |
| Research integrity | 0.035 | 0.053 |
| Insufficient payload (model declined to judge) | 0.005 | 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".