Power, Control, and Marital Violence; Beliefs vs. Behavior. A Test of the Graham-Kevan Archer Measure
Bibliographic record
Abstract
We argue that Western conceptualizations of a common couple violence/intimate terrorism divide in domestic violence categories may be ill-suited to the Korean context because they are rooted in psychopathological explanations of control motivation (Holtzworth-Munroe & Stuart, 1994; Johnson, 2008). Control motivation in Korea may be more related to the cultural necessity of keeping face in a normatively patriarchal context rather than the attachment issues suggested by Holtzworth-Munroe and Stuart (1994). To examine the power and control context of domestic violence in Korea, we implemented Graham-Kevan and Archer's (2003) measure on a sample of 77 Korean students at an elite university in Seoul. We used cluster analysis to separate the sample into high and low control cluster families. The high control cluster was associated with more domestic violence, more violence by the husband, more injuries from violence, and marginally more child abuse. Contrary to our prediction, being in the high control cluster appears to be a more important predictor of domestic violence than patriarchal beliefs. Implications and limitations are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".