A linguistic inquiry and word count analysis of the Adult Attachment Interview in two large corpora.
Bibliographic record
Abstract
s = 826 and 857) and a broader range of linguistic variables, as well as examining associations of LIWC-derived AAI dimensions with key developmental antecedents. First, regression analyses revealed that dismissing states of mind were associated with transcripts that were more truncated and deemphasized discussion of the attachment relationship whereas preoccupied states of mind were associated with longer, more conflicted, and angry narratives. Second, in aggregate, LIWC variables accounted for over a third of the variation in AAI dismissing and preoccupied states of mind, with regression weights cross-validating across samples. Third, LIWC-derived dismissing and preoccupied state of mind dimensions were associated with direct observations of maternal and paternal sensitivity as well as infant attachment security in childhood, replicating the pattern of results reported in Haydon, Roisman, Owen, Booth-LaForce, and Cox (2014) using coder-derived dismissing and preoccupation scores in the same sample.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".