Interviewing and validity issues in self-report research with incarcerated offenders: the Quebec inmate survey
Bibliographic record
Abstract
Self-report research on incarcerated offenders has had its share of support and criticism. The present chapter provides evidence from a survey of over 250 offenders that were incarcerated in federal penitentiaries in Quebec between 2000 and 2001. The principal aim of the survey was to gather data on offenders’ criminal earnings during a three-year window period. The focus for this chapter will be on various aspects surrounding the self-report survey. First, site selection and access is discussed, with a particular focus on variations across minimum, medium and maximum security level penitentiaries. Second, the logistics of respondent solicitation are addressed, with a special outlook placed on the more personal techniques for persuading inmates to take part in a survey. Third, the questionnaire and sample designs are outlined. Fourth, key variables, such as the criminal earnings measures, are described. These variables are subsequently validated for their overall form, their internal consistency and their correlation with respondents’ self-perceptions and expectations. The chapter concludes with a discussion of the limits surrounding the inmate survey and an overall appraisal of its contribution to criminological research.
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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 | 0.051 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".