You Can’t See the Real Me: Attachment Avoidance, Self-Verification, and Self-Concept Clarity
Bibliographic record
Abstract
Attachment shapes people's experiences in their close relationships and their self-views. Although attachment avoidance and anxiety both undermine relationships, past research has primarily emphasized detrimental effects of anxiety on the self-concept. However, as partners can help people maintain stable self-views, avoidant individuals' negative views of others might place them at risk for self-concept confusion. We hypothesized that avoidance would predict lower self-concept clarity and that less self-verification from partners would mediate this association. Attachment avoidance was associated with lower self-concept clarity (Studies 1-5), an effect that was mediated by low self-verification (Studies 2-3). The association between avoidance and self-verification was mediated by less self-disclosure and less trust in partner feedback (Study 4). Longitudinally, avoidance predicted changes in self-verification, which in turn predicted changes in self-concept clarity (Study 5). Thus, avoidant individuals' reluctance to trust or become too close to others may result in hidden costs to the self-concept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".