Bibliographic record
Abstract
Electronic monitoring (EM) is a generic term for a number of remote surveillance technologies — radio frequency (RF) curfew checking at single locations, biometric voice verification at single or multiple locations, remote alcohol monitoring (home-based breathalysers or mobile sobriety bracelets which measure the presence of alcohol transdermally) and Global Positioning System (GPS) satellite tracking which monitors mobility and/or the perimeter of specified exclusion zones — which have been used to extend the range of spatial and temporal (and to some degree behavioural) regulations that can be imposed on offenders under supervision in the community. One or other of the technologies can be used at the pre-trial, sentence or post-release phase of the criminal justice process, sometimes with a view to achieving reductions in the use and cost of imprisonment. They can be applied to a wide range of offenders or suspects, as a stand-alone measure for low-risk people, or as a component of an intensive supervision programme for higher-risk people. Singly or in combination — EM curfews/home detention predominate — they have been used in approximately 40 countries around the world, beginning in the United States in the early 1980s, spreading to Canada, Australia and Europe, and most recently to Korea, Latin America and Saudi Arabia. They have been embedded in many different legal, administrative and discursive frameworks, reflecting different penal cultures and traditions, which have variously inflected EM as a new tough punishment, as an aid to rehabilitation or, more neutrally, as an additional layer of control which can serve either punitive or rehabilitative ends (Nellis et al. 2012). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".