Measurement memo I: Updated practices in psychological measurement for sexual scientists
Bibliographic record
Abstract
The validity of psychological measurement is a crucial auxiliary theory underlying many sexual science studies. Although many sexuality researchers are familiar with certain elements of psychological measurement, the field of psychological measurement is a developing and evolving literature, with concepts, applications, and techniques that do not always trickle down quickly into interdisciplinary fields like sexual science. The purpose of this Measurement Memo, therefore, is to connect sexual scientists to measurement-related issues, explanations, and resources that they may not otherwise encounter in their scholarly reading. Our review focuses on those carrying out psychological measurement using theories and methods of latent variable modeling, and we identify and summarize key ideas and references that serve as good launching points for sexual scientists to begin to improve their psychological measurement practices, for beginners and seasoned users alike.
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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 | 0.036 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".