Standing at the Intersection of Identity and Convict Criminology: A Brief Exercise in Reflexivity
Bibliographic record
Abstract
The fi rst Convict Criminology (CC) session took place in 1997 at the American Society of Criminology's (ASC) annual meeting.The session was organized by members with personal experiences with the correctional system as formerly incarcerated (FI), as well as their allies.There was consensus among the group that many of the teachers in corrections had little, if any, experience in jails or prisons and lacked knowledge of what really took place behind their walls.In 2001, Richards and Ross defi ned the purpose and practice of CC, suggesting that those with fi rst-hand knowledge can provide an informed perspective on the functions and eff ects of prisons and jails.Merging insider knowledge, personal experience, and academic research related to criminal justice provides a paradigmatic approach that off ers distinct and relevant perspectives (Richards and Ross, 2001;Ross and Richards, 2003).In 2020, the Division of Convict Criminology (DCC) offi cially became part of the ASC.The original CC group was not particularly diverse, but over the last decade, the membership of CC has become more diverse in gender, sexuality, race and ethnicity, and FI background.The intersection of these identities among the members further increases the perspective and diversity of this group.As a CC member and the fi rst vice-chair of the DCC, supporting the mission and goals of the organization -including building diversity -are particularly important to me.Diverse experiences and voices support CC's mission to support justice-impacted scholars in providing rigorous research that examines all aspects of the criminal justice system, including policing, courts, and corrections, from those who have lived experiences within the fi eld (Tietjen, 2019).The DCC has explicitly addressed this issue, arguing that those in academia have largely ignored research from those who are formerly incarcerated or have had direct contact with the system.Acknowledging that the relevance of research conducted by incarcerated or FI individuals is often overlooked is signifi cant to the DCC's (2021) purpose: …to provide an intellectual home for all scholars/scientists who are interested in the study of Convict Criminology.The members of the 56 Journal of Prisoners on Prisons, Volume 33(1), 2024 DCC are students, researchers, and faculty members from diverse backgrounds-both with and without criminal records-that mentor formerly-incarcerated students and junior faculty, conduct relevant research, and advocate for progressive justice reform for formerly incarcerated individuals and all of society.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.012 | 0.027 |
| 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".