Bibliographic record
Abstract
Deviant behaviours are a significant cost to Canadian society and can incur an immeasurable amount of emotional and physical damage every year (Office of the Parliamentary Budget Officer, 2018; The John Howard Society of Canada, 2018). There have been numerous studies on the role of risk factors in affecting deviant behaviours, however, few of these have examined the influence of self-determination on deviance (Mann et al., 2010; Murray & Farrington, 2010; Zara & Farrington, 2010). This study intends to fill this gap by investigating the interactions between self-determination, gender, risk factors, and deviance. Participants were invited through the University of Saskatchewan’s PAWS and SONA systems to complete an online survey that asked questions relating to gender, self-determination, risk factors, and deviance. A Chi-square Test for Independence was utilized to explore the explicit relationships between the type of self-determination and gender differences. In addition, a two-way MANOVA was used to compare self-determination and gender together in relation to deviance and risk factors. A Chi-square test found that there was not a significant relationship between gender and self-determination while the two-way MANOVA found a significant interaction effect between self-determination, deviance, and risk factors. However, when the interaction was examined further through univariate ANOVAs, no significant differences were found. Future research that examines and expands on the relationship between self-determination, gender, risk factors and antisocial behavior is suggested.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".