You Should be Flattered: An Examination of the Non-Reporting of Stalking Victimization
Bibliographic record
Abstract
Reports from the 2014 Canadian Social Survey (GSS) on Victimization show that only 39% of individuals who have been a victim of stalking had reported their victimization to law enforcement, leaving an astounding 61% of victims to cope with their victimization in silence. Stalking is an interpersonal crime that is highly invasive, yet many victims are reluctant bring to law enforcement. To create a safer Canada, there is a desperate need to address why victims of stalking do not notify law enforcement of their victimization. The main objectives of this thesis are to identify relationships between stalking victimization and Canadian demographics, as well as to close a gap in literature exploring stalking victimization and non-reporting patterns. To achieve these objectives, the Canadian General Social Survey (GSS) – Victimization (2014) is used to analyze respondent accounts of their demographic, stalking victimization, and the reasons why they chose not to report their victimization to law enforcement. Findings show that there are three main categories of non-reporting behaviours: 1. Intrapersonal Perceptions; 2. Interpersonal Relationships; and 3. Distrust with the Criminal Legal System.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".