The Evolution of Domestic Abuse as a Process (DAP) Model: An Initial Statement
Bibliographic record
Abstract
A new model of intimate partner violence, the Domestic Abuse Process (DAP) model, is presented to address how domestic abuse emerges, evolves, and escalates in a romantic relationship over time. A review of the relevant literature on intimate partner violence, including studies examining the role of resources, relationship goals and means for achieving these goals, and relationship stressors is conducted. Important theories such as symbolic interactionism, strain, intergenerational transmission of violence, and the process model of family violence are also reviewed and discussed within the context of domestic abuse. A short discussion of how the proposed model could be empirically tested using a survey instrument containing numerous items that are administered to respondent couples is provided. Follow-up interviews with respondent couples would be used to clarify survey responses and to obtain more detailed insights into how abuse entered and intensified in respondent relationships. Both quantitative and qualitative analyses would be performed on the subsequent data to glean important factors and patterns empirically involved in the process. The model provides additional insights into intimate partner violence and abuse that could inform treatment practices and policy.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".