Connect or protect? Social class and self-protection in romantic relationships.
Bibliographic record
Abstract
Lower SES (socioeconomic status) couples tend to face particular challenges in their relationships. Relative to higher SES couples, they are less likely to marry and more likely to divorce-but they do not value their romantic relationships any less. Drawing on risk regulation theory and theories of social class as culture, we suggest that lower SES individuals adapt to their more chronically precarious environments by prioritizing self-protection more than higher SES individuals do, but that the need to self-protect may undermine relationship satisfaction. We investigate these ideas across 3 studies, using cross-sectional, longitudinal, and daily-diary methods. Lower SES individuals were more self-protective, both in their thoughts about their relationship (Studies 2-3), and in the judgments they made about their partner's commitment level over 2 years (Study 1) and 2 weeks (Study 3). Self-protection, in turn, was associated with lower relationship satisfaction (Studies 2-3). However, lower SES individuals were only self-protective when feeling vulnerable in their relationships (Study 3). Taken together, these studies identify psychological mechanisms to explain why the structural challenges that lower SES individuals experience can make it more difficult to achieve satisfying relationships. (PsycInfo Database Record (c) 2022 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".