A Credibility Revolution for Relationship Science: Where Can We Step Up Our Game?
Bibliographic record
Abstract
ABSTRACT The discipline of psychology is undergoing a credibility revolution whereby researchers are critically evaluating and improving their research practices. In this review, we consider how the field of relationship science could capitalize on this movement in the context of four types of validity. Regarding statistical‐conclusions validity, we find that relationship scientists are engaging in open science practices (e.g., preregistration, open data sharing) at similar rates to other fields in the context of personality and social psychology journals. However, journals that are specific to the field (i.e., close relationships journals) could do more to encourage these practices. Meanwhile, new meta‐scientific research suggests that the field would benefit greatly from rigorous, widescale measurement validation work (construct validity), novel strategies to account for causal confounds (internal validity), and more diverse representation in our samples and measures (external validity). Overall, the credibility revolution offers several specific, actionable recommendations to improve the validity of research findings, many of which are highly relevant to relationship science.
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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 | 0.161 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.020 | 0.071 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.020 | 0.037 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".