Bibliographic record
Abstract
The study of partner abuse has been documented in the literature over the past 30 years. However, it is only in the last decade that studies have started to focus on partner abuse in gay male relationships. Most of the quantitative research has focused on prevalence and incidence rates. Although correlates of gay partner abuse has been studied it has not been guided by theory. Most of the theories about gay partner abuse have been based on heterosexual partner abuse models. Recent trends in theory development have suggested that a number of theories be combined to create a multi-integrated model of partner abuse. The current study tested this trend by surveying 155 men across North America (i.e., Canada and the United States) on a variety of variables established or hypothesized by the literature to influence gay male partner abuse. These variables acted as indicators to four factors that this study has suggested will act in concert to influence partner abuse in gay men. These four factors that were studied were: Clinical, Socio-Political, Orientation-Specific, and Previous Social Learning. The Clinical Factor concerned itself with indicator variables that were related to personality features and alcohol use, while the Socio-Political Factor focused on indicators of perception of power and satisfaction. The Orientation-Specific Factor had indicator variables related to minority stress and attitudes towards myths of same-sex partner abuse, while the Previous Social Learning Factor was concerned with experiencing child abuse or exposure to domestic violence. This study analyzed whether these factors had an effect on Partner Abuse via Structural Equation Modeling. This analysis found that the Clinical, the Orientation-Specific and the Previous Social Learning Factors had a direct effect on Partner Abuse. Additionally, the Previous Social Learning Factor not only had a direct or proximal effect on Partner Abuse, it also had an indirect or distal impact via Orientation-Specific Factor. The Socio-Political Factor did not have a significant effect on Partner Abuse, but Previous Social Learning had an effect on Socio-Political. The relationships between these factors and their effect on Partner Abuse were delineated. Implications for theory, practice and future research are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".