Assessing the Relationship between Antisocial Personality Traits and Cyberstalking in a Canadian Adult Sample
Bibliographic record
Abstract
Cyberstalking is defined as the repeated pursuit of an individual using the Internet (Reyns et al., 2012). Cyberstalking results in detrimental effects for victims including both physical and psychological issues (Dreßing et al., 2014; Worsley et al., 2017). Identifying risk factors for this behaviour is therefore an important step in developing prevention efforts. Dark Triad traits have been examined as correlates to cyberstalking (Smoker & March, 2017); however, this research has several limitations including the use of short, truncated measures of Dark Triad traits that have been criticized for failing to distinguish between psychopathy and Machiavellianism, failing to account for the multi-faceted nature of each construct, and utilizing multivariate models that are difficult to interpret (see Miller et al., 2019). The goal of this study is to examine the relationship between each Dark Triad construct and cyberstalking while addressing criticisms of existing Dark Triad research. A sample of 1725 Canadian citizens was recruited online through Qualtrics panels in May of 2020. The sample was matched to the broader Canadian population in terms of age, sex, and income. Participants completed the Self-Report Psychopathy scale Short Form (SRP-4 SF; Paulhus et al., 2015), the Five Factor Machiavellianism Inventory (FFMI; Collison et al., 2018), the Narcissistic Grandiosity Scale (NGS; Rosenthal et al., 2020), the Narcissistic Vulnerability Scale (NVS; Crowe et al., 2018), and indicated their engagement in cyberstalking behaviours. Binomial logistic regression will be used for determining the relationship between each Dark Triad construct and any engagement in cyberstalking behaviour (yes/no). Baseline rates for cyberstalking will also be provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".