Every Picture Tells a Story: Framing and Understanding the Activism of Convict Criminology
Bibliographic record
Abstract
Convict Criminology (CC) consists of three major initiatives. Although scholarship and mentoring have been dominant activities, understanding the activism/policymaking of CC is less well known. This paper reviews the primary United States based activities that CC has done in this area and suggests what it needs to do to assist the interests of individuals who arebehind bars and those who are formerly incarcerated, as well as work towards the mission of the CC organization as a whole. Some of the areas where CC has participated politically include the news-making we have done (i.e. interviews with the news media) and the periodic statements released on social media by the American Society of Criminology’s Division of Convict Criminology. This paper will also consider the notion of praxis as applied to CC, in that some members consider their research, public speaking, and mentorship to be political actions worthy to be considered political activity.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.034 | 0.078 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".