Ethical Dilemmas in International Criminological Research
Bibliographic record
Abstract
Full participant observation studies in prison research are rare and privileged and bring with them ethical considerations. In the current article, we reflect on the ethical issues and dilemmas within two independent, but strikingly similar, research studies that took place almost 20 years apart: one in the United Kingdom in 2002 and one in Canada in 2019. In both studies, the researchers completed the initial training programme for new correctional/prison officer recruits. We discuss here the tenets and nuances of participant observation, reflecting on the applicability of traditional ethical concerns such as consent, disclosure, and deception as well as the professional and intrapersonal demands and implications of immersive ethnography on the researcher identity and how we managed our role. The key methodological lesson we both learnt was that immersion does not mean jeopardy of objectivity and research integrity, but immersion can place not insignificant personal and professional demands on those undertaking such involved ethnographic work.
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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 | 0.096 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.075 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| 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".