Bibliographic record
Abstract
Trust is widely regarded as a fundamental psychological concept in the study of relationships yet—rather than being investigated as a primary, material focus—is often relegated to serving as a proxy for and/or a means to facilitate the exploration of aspects of relationship quality and functioning. We conducted two measurement-focused studies to garner deeper insight into the nature of trust in romantic relationships and explore an account of trust that reframes its development as a process of construction, rather than addition. In Study 1 ( N = 494), we explored the nature and structure of the construct of trust as put forward by Rempel et al. (1985) . In Study 2 ( N = 847), we then confirmed our findings from Study 1 and, using measurement invariance techniques and a refined version of Rempel et al.'s (1985) assessment, investigated the extent to which the trust of individuals involved in newly-formed relationships ( n Newly-formed = 387) versus individuals involved in long term relationships ( n Long-term = 460) reflect different conceptualizations of the construct. Across both studies, we (a) identified a compilation of items reflecting key factors of trust—Predictability (3 items), Dependability (4 items), and Faith (10 items)—resembling Rempel et al.'s (1985) framework; and (b) found broad sameness in the construct of trust across newly-formed and long-term relationships but also some granular differences that potentially suggest how the construct may change across stages of relationship development.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".