The ex-factor: Characteristics of online and offline post-relationship contact and tracking among Canadian emerging adults
Bibliographic record
Abstract
The breakup of an intimate relationship is a highly distressing event among emerging adults (Cutler, Glaeser, Norberg, 2001) and can often be accompanied by difficulty adjusting to the loss and “letting go” (Mearns, 1991). Research on stalking and cyberstalking behaviours address criminal activities that incite fear in a target (e.g., Spitzberg & Cupach, 2007). Little is known about more general post-relationship contact and tracking (PRCT), that is, efforts to maintain or re-establish contact with an ex-partner or to track their whereabouts, new partnerships or activities. To understand both the use and experience of PRCT, we examined reports from 271 Canadian emerging adults (aged 18–25) regarding their most recent breakup within the prior year. Results indicated that online and offline forms of post-relationship contact and tracking were common, characterizing 87.8% of all recent breakups, and were typically used in conjunction. In fact, online forms rarely occurred in isolation. Attempts to keep in contact were most commonly reported by users and targets of behaviours, whereas extreme and threatening behaviours that might comprise stalking or cyberstalking were rare. No gender differences were found in the use of PRCT behaviours, although women reported experiencing more offline forms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".