Stalking: Defining and prosecuting a new category of offending
Bibliographic record
Abstract
The purpose of this paper is to examine the development of anti-stalking laws and the various legal definitions that have been applied to stalking in the United States, Canada, Australia, and the United Kingdom. Specifically, we examine the attempts that have been made to limit the offence to prevent inadvertently making legitimate activities illegal and the relative importance that different jurisdictions have placed on the intentions of the stalker versus the reactions of the victim. Furthermore, the advantages and disadvantages of antistalking legislation are analysed, placing emphasis on the proper application and the potential for misuse of these contentious laws. These laws have, undoubtedly filled a gap in the criminal and civil law that previously permitted effective intervention only after a harasser had caused physical harm to the victim and that largely ignored the enormous potential harm inflicted by inducing persistent fear and apprehension in the victim. Whether these laws prove sufficient to effectively prevent and punish stalking is questionable. This may ultimately depend not only on the motivations and psychiatric status of the offender, but also on the willingness of the criminal justice system to view the offence seriously.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".