Bibliographic record
Abstract
Among prison scholars it is well known that access to penal institutions for the purposes of conducting research is not a given. For instance, in the Canadian context, some social researchers have been effectively barred from conducting studies inside prisons or have had to modify their research designs in order to enter the carceral. The ability to obtain unpublished records on imprisonment policies and practices in Canada has also been cited as a cumbersome process that often results in non-disclosure of the documents sought. Beyond data collection, social researchers have also raised concerns about the challenges of communicating their findings to publics outside the academy. In criminology, in particular, scholars have been concerned with the perceived lack of influence academic work has had on public policy and public opinion. These interventions, while not novel, have resulted in calls for a public criminology, renewing a discussion on how to disseminate research to non-academic audiences. Although much of the access to information literature is focused on the techniques used to obtain data as well as the barriers encountered during the process, and the public criminology literature is centred principally around the question of how to reach and influence those outside the halls of the university, few have examined how data collection and dissemination activities shape subsequent information flows. Here, I am not referring to the moments when and sites where the “policing of criminological knowledge” occur that mediate access to data sources and diffusion opportunities based on the epistemological orientations and political agendas of gatekeepers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.267 | 0.454 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.031 | 0.070 |
| Scholarly communication | 0.067 | 0.091 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".