Library crime and security : an international perspective
Bibliographic record
Abstract
This invaluable book provides information and guidelines that will be of great practical use to all library professionals concerned with the problem of crime in their libraries. Library Crime and Security focuses on the patterns of library crime and disruption in Great Britain, Canada, and the United States. In addition to important data on these probelsm, there is extensive information on the characteristics of the institutions and the communities in which they are found. The impact of crime on the institution and the individual is examined.The authors present vital insights into the design of crime control programs in libraries of varying sizes that have or anticipate problems with crime such as book theft, vandalism, problem patrons, and attacks against staff. Major issues in the measurement, incidence, and consequences of crime are included, as well as relevant materials from the fields of library science, management, criminology, victimology, and security. An extensive security checklist is included that can serve as a guide for making the library a safer and more secure setting for staff, patrons, and contents. Library Crime and Security is essential reading for library administrators and staff of all libraries.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".