A Behavior Sequence Analysis of Victims’ Accounts of Stalking Behaviors
Bibliographic record
Abstract
Stalking is a complex issue involving multiple behaviors and interactions between the stalker and their target. Research has typically involved grouping risk behaviors related to stalking; however, the research question in the current research was to what extent a temporal method would allow investigators to map the dynamics of stalking. Behavior Sequence Analysis is a form of systems analysis that examines sequences of events over time, providing statistically significant results from complex real-world data. The Behavior Sequence Analysis method was applied to 39 participants' detailed accounts of stalking written in online forums. The study provides illustration of the antecedents of stalking and how it may initiate and develop through to end of contact. Both stalker behavior and decisions made by victim were included in the models. The results show multiple patterns of stalkers' behaviors; however, the results also clearly show that victims need not perform many behaviors for stalkers to continue with their actions. A main finding was how many behavior transitions occurred before victims felt a significant problem. A large number of participants indicated that they (repeatedly) reported their case of stalking to police and authorities; however, they were mostly dismissed or felt that police did not stop the stalker's actions. A major implication of the current research is providing a novel method to produce a framework that may be used to operationalize definitions of stalking based on coherent frameworks of stalkers' behaviors over time.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".