Bibliographic record
Abstract
The first part of the module measured public knowledge and attitudes about youth crime. It began with questions designed to provide some context within which to locate these attitudes. At the start of the module – before they had become sensitised to the issue – respondents were asked how serious a problem youth crime was, relative to other crime problems. The question was asked in two different ways. Half the respondents were given a show card with a list of five crime problems, and were asked to identify the most serious problem. Providing respondents with response options in a forced choice format has the disadvantage of prejudging the options. There are two risks with this approach. Having been prompted, respondents may support an option that would not have occurred to them unprompted. On the other hand, the questionnaire necessarily limits the range of options open to respondents, and may actually rule out preferred options for some respondents. We therefore asked the other half of the sample to specify the most important crime problem without being provided with options, in an open-ended format. Table 2.1 shows that when participants were provided with response options, no single crime problem was identified by a majority of respondents1. Drug-related crime was the problem identified as the most important by the largest minority (35% of the sample). Crime by sex offenders was identified by just over one quarter of the sample, and terrorist crime by 16%2. Crime by young offenders was the fourth most frequently cited response, accounting for 15% of the sample.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".