How to Produce, Identify, and Motivate Robust Psychological Science: A Roadmap and a Response to Vize et al.
Bibliographic record
Abstract
Some wish to mandate preregistration as a response to the replication crisis, while I and others caution that such mandates inadvertently cause harm and distract from more critical reforms. In this article, after briefly critiquing a recently published defense of preregistration mandates, I propose a three-part vision for cultivating a robust and cumulative psychological science. First, we must know how to produce robust rather than fragile findings. Key ingredients include sufficient sample sizes, valid measurement, and honesty/transparency. Second, we must know how to identify robust (and non-robust) findings. To this end, I reframe robustness checks broadly into four types: across analytic decisions, across measures, across samples, and across investigative teams. Third, we must be motivated to produce and care about robust science. This aim requires marshaling sociocultural forces to support, reward, and celebrate the production of robust findings, just as we once rewarded flashy but fragile findings. Critically, these sociocultural reinforcements must be tied as closely as possible to rigor and robustness themselves-rather than cosmetic indicators of rigor and robustness, as we have done in the past.
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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 | 0.706 | 0.863 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.011 | 0.031 |
| Research integrity | 0.043 | 0.066 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".