Bibliographic record
Abstract
Failures to replicate published psychological research findings have contributed to a "crisis of confidence." Several reasons for these failures have been proposed, the most notable being questionable research practices and data fraud. We examine replication from a different perspective and illustrate that current intuitive expectations for replication are unreasonable. We used computer simulations to create thousands of ideal replications, with the same participants, wherein the only difference across replications was random measurement error. In the first set of simulations, study results differed substantially across replications as a result of measurement error alone. This raises questions about how researchers should interpret failed replication attempts, given the large impact that even modest amounts of measurement error can have on observed associations. In the second set of simulations, we illustrated the difficulties that researchers face when trying to interpret and replicate a published finding. We also assessed the relative importance of both sampling error and measurement error in producing variability in replications. Conventionally, replication attempts are viewed through the lens of verifying or falsifying published findings. We suggest that this is a flawed perspective and that researchers should adjust their expectations concerning replications and shift to a meta-analytic mind-set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.699 | 0.887 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 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".