Bibliographic record
Abstract
Evidence-based policy-making implies greater clarity in the relationship between science, politics and crime control. This is especially the case with a highly polarizing topic like gun-crime. Specifically, the enrolment of social science by pressure groups, political parties and other political actors raises questions about the possibility and desirability of a scientifically detached appraisal of the problem. One resolution is to reject the feasibility of objective detachment, treat science and politics as synonymous and locate criminology firmly in the domain of politics and morality—to `take sides' as it were. This renders the purpose of academic criminology problematic, for if its practitioners are to be regarded as inevitably partisan, what do they contribute as social scientists to public issues defined as political and moral in content? Why should criminological knowledge claims be especially valued over that of other political and moral actors? More recently, attempts to define concepts about the formative intentions, intrinsic and extrinsic to the politics of scientists' work, suggest ways of demarcating science from politics in this and other criminological disputes. They provide a rationale for the distinctive contribution of social science to public controversies over crime and control.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".