Accommodating Complexity: Adapting Accommodation Theory to Capture Responses to Specific Transgressions
Bibliographic record
Abstract
Sooner or later, we are all going to be hurt by the ones we love. Though we cannot wholly prevent such transgressions from occurring, we may be able to control how we respond, and those responses can help determine the outcome of the transgression, for good or ill. One of the most prominent models for understanding how individuals respond to transgression has been Rusbult’s EVLN model, a two-dimensional typology with four categories: Exit, Voice, Loyalty and Neglect. Despite its usefulness, this typology is limited in important ways, which prompted me to re-examine and re-calibrate the EVLN. In this dissertation, I present two studies designed to describe how individuals can respond to specific transgressions from a romantic partner (rather than responses to relationship dissatisfaction, as the EVLN was initially designed to do). In these studies, I asked undergraduate participants to list how they would respond to several hypothetical transgressions (Study 1, Phase 1; N = 107) or community participants how they actually responded to recalled transgression from a romantic partner (Study 2, Phase 1; N = 39). I then had undergraduates generate various ratings of those responses (Study 1, Phases 2 and 3; N = 150 and 195 respectively; Study 2, Phase 2, N = 197) and used multi-dimensional scaling (MDS) techniques to assess how transgression-related responses should be organized and categorized. The result is an eight-fold typology summarized by the acronym CARE-CAMP. This typology differs from the EVLN in that it provides alternate dimensions (“avoidant” and “retaliatory”) and unique categories (e.g., “Cold-Shoulder” and “Moratorium”) that add theoretically important nuance to our understanding of accommodation in close relationships.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".