Space, Time, and Reflexive Interviewing: Implications for Qualitative Research with Active, Incarcerated, and Former Criminal Offenders
Bibliographic record
Abstract
Space and time are concepts familiar to physicists, philosophers, and social scientists; they are operationalized with varying degrees of specificity but are both heralded as important to contextualizing research and understanding individual, cultural, and historical differences in perception and the social construction of reality. Space can range from, at the macro level, geographic region, to at the micro level, the immediate physical surroundings of an individual or group of persons. Similarly, a conceptualization of time can range from era or epoch to the passing of seconds and minutes within a situational dynamic of human interaction. In this article we examine the microcosmic end of the space-time spectrum, specifically as it relates to doing qualitative interviews with current or former criminal offenders. Through a comparative discussion of interviews with incarcerated, recently released, and active offenders, we pose questions and offer insights regarding how interviewers and interviewees perceive physical space and the passage of time and, most importantly, how these perceptions relate to the interview process and resulting data. Notably, we suggest that interviewer reflexivity should take into account not only the relationship, dialogue, and discourse between interviewer and interviewee but also space and time as perceived and constructed by both parties. Finally, we offer several key strategies for incorporating these considerations into the interviewer toolkit.
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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 | 0.297 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.034 | 0.071 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".