Hitting the bull’s eye: Attachment representations and the organization of social networks
Bibliographic record
Abstract
Hazan and Zeifman were the first to explore Bowlby’s proposition that adults would organize their attachment relationships into a hierarchy and since then considerable research has explored both the structure and function of attachment hierarchies using different methodologies. In this study, previous findings establishing an association between attachment and networks were replicated and the associations between network members were explored. First, consistent with expectations, the findings provided additional evidence that romantic partners do not necessarily jump to the top of the hierarchy and young adults continue to place parents, in particular mothers, at the top of their hierarchy. Consistent with previous work, security was associated with placing others closer to the self and attachment avoidance was associated with placing others farther from the self on an electronic bull’s eye. Furthermore, to date, this is the first study to examine the association between attachment representations and the organization of network members. Interestingly, security was associated with placing network members closer to each other and attachment avoidance was associated with placing network members farther from each other. This finding suggests that individuals with high attachment security may be more motivated to allow members of their social networks to mingle whereas individuals with high attachment avoidance scores seemed to be motivated to keep their network members at a distance. The results of this study extend our understanding how attachment representations may influence the organization of our social networks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".