Metropolitan Police Public Attitudes Surveys, 2000-2017/18
Bibliographic record
Abstract
January 2019: These data have been temporarily withdrawn while the depositor conducts a review of governance around data sharing and publication. The Public Attitude Survey (PAS) is a well-established survey that was first conducted in 1983 to give the Metropolitan Police Service (MPS) an understanding of the views of residents across London. From April 2014 the Mayor’s Office for Policing and Crime (MOPAC) took responsibility for the survey, which measures Londoners' confidence in the police and provides information that helps to set the strategic direction for policing and support continuous improvement at borough level. The PAS is a continuous survey, based on a random sample of respondents at pre-selected addresses with a total of 3,200 Londoners normally interviewed face-to-face each quarter to yield an annual sample of 12,800 interviews. The survey is designed to achieve 100 interviews each quarter in the 32 London Boroughs (excluding the City of London) in order to provide a borough-level sample of 400 interviews in any 12-month rolling period. Users should note that data are not currently available for April 2004-December 2005, but commence again in 2006. For further information, see documentation and the MOPAC data and statistics webpages. Another MPS survey series, the Metropolitan Police User Satisfaction Survey, is held at the UK Data Archive under SN 7084. Latest Edition Information For the seventh edition (June 2018), data and documentation for Quarters 49-52 were added, extending the study coverage to 2017-18.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.029 |
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".