Conscientiousness and attachment avoidance as moderators of the association between attachment anxiety and neuroticism
Bibliographic record
Abstract
Crawford, Shaver, and Goldsmith ((2007) How affect regulation moderates the association between anxious attachment and neuroticism, Attachment & Human Development 9, 95–109) suggested that attachment avoidance and conscientiousness may both serve as affect regulation strategies that allow individuals to down-regulate negative emotions, and thereby attenuate the usually strong association between neuroticism and general attachment anxiety. Their findings partially supported this hypothesis. As predicted, at high levels of neuroticism, both avoidance and conscientiousness were associated with decreased levels of attachment anxiety. Unexpectedly, however, at low levels of neuroticism, avoidance and conscientiousness were actually associated with increased, rather than decreased, attachment anxiety. In the current study, we replicated Crawford et al.’s results in a sample of 160 undergraduate students, and also extended this line of research by considering relationship-specific attachment with mother, father, best friend, and romantic partner. Very different patterns of results emerged in the relationship-specific analyses. We propose a more interpersonal perspective on personality to explain the pattern of results, one that considers how neuroticism, conscientiousness, and attachment avoidance are actually enacted in everyday social contexts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".