Overcoming Ambivalence: The Challenges of Exploring Socially Charged Issues
Bibliographic record
Abstract
In this article, the author considers the challenges associated with the investigation of sensitive and socially charged issues. Drawing on a qualitative study of older women's body image and embodied experience, she discusses how societal ambivalence toward older women's bodies and appearances shapes and constrains the establishment of rapport between a young researcher and the study participants. Exploring the internalized anxiety expressed by the women, she investigates how life history narratives, multiple interviews, photographs, location of the interview, self-disclosure, information sharing, provision of assistance, student and stranger statuses, collaborative interviewing, and impression management combine to enhance rapport and ease the women's sense of discomfort.
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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 | 0.104 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.020 | 0.107 |
| Scholarly communication | 0.031 | 0.038 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.011 | 0.015 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".