“Thanks for Using Me”: An Exploration of Exit Strategy in Qualitative Research
Bibliographic record
Abstract
This article examines, through a synthesis of the literature and excerpts from a qualitative case study, the concept of exit strategy, specifically its relation to vulnerable populations (e.g., overweight adolescent boys) and potential impact on the researcher-participant relationship. The quality and duration of the researcher-participant relationship, along with rapport and trust building, are potential indicators for negotiated closure (i.e., exit strategy). Reframing this relationship as “participant-researcher” resituates vulnerable participants as foremost in such relationships. Given what is potentially at stake for participants in qualitative research, there is a moral and ethical imperative to enter into the dialogue of closure. Otherwise, participants may unwittingly serve as a means to an end, that is, as objects in the enterprise of qualitative research. Researchers, research supervisors, and human subject ethics committees are urged to establish protocols to guide how research relationships are ended within the context of qualitative methods, particularly with respect to vulnerable populations.
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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 | 0.145 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.045 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| 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".