What Works in the Assessment of Stalking Threat and Risk of Harm
Bibliographic record
Abstract
This chapter highlights the complexities of stalking risk assessment and provides a summary of the empirical support for stalking risk assessment tools, and outlines the risk factors associated with different outcomes of stalking perpetration. Several risk assessment measures have been designed to specifically assess risk among those who have engaged in stalking offences. As iterated, many of the assessment tools require evaluators to make different clinical judgements based on outcome type. The chapter provides a summary of the available literature on these outcomes, namely, persistence, escalation to physical harm and continued stalking. Stalking most commonly results in serious psychological harm – emotional distress, disruption of daily activities and so forth. There is a need to expand the field to incorporate the assessment and study of cyberstalking and adolescent stalking. Research should examine the management of cyberstalking given its international nature and the barriers inherent in working with international technology companies and platforms where cyberstalking occurs.
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 | 0.053 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".