Settling for less out of fear of being single.
Bibliographic record
Abstract
The present research demonstrates that fear of being single predicts settling for less in romantic relationships, even accounting for constructs typically examined in relationship research such as anxious attachment. Study 1 explored the content of people's thoughts about being single. Studies 2A and 2B involved the development and validation of the Fear of Being Single Scale. Study 2C provided preliminary support for the hypothesis that fear of being single predicts settling for less in ongoing relationships, as evidenced by greater dependence in unsatisfying relationships. Study 3 replicated this effect in a longitudinal study demonstrating that fear of being single predicts lower likelihood of initiating the dissolution of a less satisfying relationship. Studies 4A and 4B explored the predictive ability of fear of being single for self-reported dating standards. Across both samples, fear of being single was unrelated to self-reported standards for a mate, with the exception of consistently higher standards for parenting. Studies 5 and 6 explored romantic interest in targets that were manipulated to vary in responsiveness and physical attractiveness. These studies found that fear of being single consistently predicted romantic interest in less responsive and less attractive dating targets. Study 7 explored fear of being single during a speed-dating event. We found that fear of being single predicted being less selective in expressing romantic interest but did not predict other daters' romantic interest. Taken together, the present research suggests that fear of being single is a meaningful predictor of settling for less in 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".