For my eyes only: Gaze control, enmeshment, and relationship quality.
Bibliographic record
Abstract
Perceived closeness that preserves the distinctness of each partner enhances intimate relationship quality, whereas pseudocloseness or enmeshment--reflecting an inability to distinguish one's own thoughts and emotions from a partner's--may have more negative outcomes (R. J. Green & P. D. Werner, 1996). Two studies investigated whether a dispositional inability to differentiate self from other is manifested at the attentional level as reduced capacity to inhibit following the gaze of another (A. Frischen, A. P. Bayliss, & S. P. Tipper, 2007). Among healthy elderly spouses in Study 1, superior gaze control predicted superior sociocognitive functioning, and those with poorer gaze control abilities were perceived by the partner as constricting the perceiving partner's autonomy, which in turn predicted lower relationship satisfaction among the latter. Moreover, these links were mediated by enmeshment, as indicated by the percentage of "we"-focused versus "I"- or partner-focused thoughts and emotions in the partners' independent accounts of the same relationship events. Extending these findings in a sample of Parkinson's disease patients and their spouses, Study 2 revealed a biphasic effect of self-other differentiation on relationship dynamics: In the early stages of the disease, increased couple focus promoted superior relationship quality, whereas lack of self-other differentiation predicted poorer relationship quality later. Thus, dispositional variations in fundamental social-perceptual processes predict both close relationship dynamics and long-term relationship quality.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".