Bibliographic record
Abstract
Technology usage is at an all-time high in Canada, resulting in almost all Canadians, under the age of 45, using the internet everyday (Statistics Canada, 2017).Recently, it has been highlighted that intimate partners are exposed to various vulnerabilities online.Individuals can be victimized by their intimate partners by means of cyber acts, called cyber aggression.Cyber aggression is characterized as threatening, insulting or humiliating acts intended to cause distress, such as sending embarrassing photos or videos over the Internet, using an intimate partner's passwords to access their social media and email to spy, or the using technology to exhibit control over one's partner (Borrajo et al., 2015;Buesa & Calvete, 2011;Watkins et al., 2016; Wright, 2017).There are two central types of cyber aggression: direct and control monitoring (Borrajo et al., 2015).Currently, cyber aggression among intimate partners has been scarcely examined in the literature.To address intimate partner cyber aggression, three foundational areas were examined in this dissertation, (1) who might predict intimate partner cyber aggression, (2) why perpetrators employ these behaviours, (3) and what the associations are between intimate partner cyber aggression perpetration and well-being.Both quantitative and qualitative research methods were employed to holistically address this topic.In an attempt to answer the question "who" perpetrates this form of aggression, an association between insecure attachment characteristics and partner directed cyber aggression was discovered.While exploring "why" individuals perpetrate intimate partner cyber aggression, six underlying motives were revealed.Lastly, while answering the question "what" is the relationship between cyber aggression perpetration and well-being, an association between mental health symptoms and relationship investment was discovered.Given technology's continuous advancements, the engagement in online aggressive behaviours also uniformly progress, making this research topic relevant and time sensitive.These Intimate Partner Cyber Aggression iii research findings advanced the scientific literature tremendously as the results created a foundational knowledge for future research to build from.Additionally, this dissertation has clinical implications that can be implemented immediately, which is imperative given the recent reliance on technology as a result of the COVID-19 pandemic.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".