Reflexivity: Interviewing Women and Men Formerly Addicted to Drugs and/or Alcohol
Bibliographic record
Abstract
This article considers how one researcher used reflexivity in two research projects. Qualitative research often involves a consideration of sensitive topics, one which may include research with individuals formerly addicted to drugs and/or alcohol. However, there is little in the literature that focuses directly on such experiences for researchers in this field; that is, a consideration of how a researcher might use reflectivity while interviewing those formerly addicted to substances. Exploring the following themes, I highlight how I reflected on the experiences that my participants (25 women and 25 men) revealed about their stories of their addiction and recovery processes: (1) my personal characteristics and my background work; (2) the importance of documenting power balance or power imbalance in my research; (3) documenting the unexpected; and (4) reflecting on the impact of my interviews/field notes.
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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 | 0.054 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".