Bibliographic record
Abstract
Paul Rock began studying sociological criminology in 1961 and his intellectual history has run parallel to and in conversation with the evolution of the discipline over that long period. He became a professional scholar when symbolic interactionism, sociological phenomenology and 'labelling theory' were taking form within criminology, and it is to those ways of viewing the social world that he still clings, although he has sought also to reflect critically upon them as time went by. Having completed a DPhil dissertation on debt collection as a moral career, and largely as a matter of serendipity, he was to take to empirical research just as policies for victims of crime were being developed by governments across the developed world and, finding himself embedded as a visitor in a Canadian federal criminal justice ministry when a federal-provincial task force was being mooted, he was able to embark on the first of a sequence of field studies of policy-making centred chiefly on victims. Those two interlaced preoccupations, theoretical and empirical, continually informed much, if not all, of his subsequent work, contributing to what has been, in effect, a running series of comparative ethnographies of government decision-making about the role of the victim in and around the criminal justice system.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".