A Large-Scale Test of the Replicability and Generalizability of Survey Measures in Close Relationship and Sexuality Science
Bibliographic record
Abstract
When measurement models are not replicable and/or generalizable, clinical assessments become of questionable utility, and unreplicable findings from studies using those measures will follow. Inspired by recent examinations of measurement in neighboring fields of psychology, we propose a Registered Report, in order to evaluate the replicability and generalizability of 20 well-known and emerging measures assessing elements of romantic relationships and sexuality. After collecting a large sample of that is both sexually and relationally diverse, we will evaluate the taxometric structure, measurement model replicability, reliability, and generalizability of each measure across a multitude of theorized sources of noninvariance. Our results are likely to be of high value to clinical researchers and practitioners alike, as we identify which measures can produce credible assessments, while simultaneously revealing measures with limited replicability and/or generalizability, as well as relational and sexual concepts for which groups may have radically different mental constructions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".