Bibliographic record
Abstract
In recent years, there has been a growing concern regarding the replicability of findings in psychology, including a mounting number of prominent findings that have failed to replicate via high-powered independent replication attempts. In the face of this replicability “crisis of confidence”, several initiatives have been implemented to increase the reliability of empirical findings. In the current article, I propose a new replication norm that aims to further boost the dependability of findings in psychology. Paralleling the extant social norm that researchers should peer review about three times as many articles that they themselves publish per year, the new replication norm states that researchers should aim to independently replicate important findings in their own research areas in proportion to the number of original studies they themselves publish per year (e.g., a 4:1 original-to-replication studies ratio). I argue this simple approach could significantly advance our science by increasing the reliability and cumulative nature of our empirical knowledge base, accelerating our theoretical understanding of psychological phenomena, instilling a focus on quality rather than quantity, and by facilitating our transformation toward a research culture where executing and reporting independent direct replications is viewed as an ordinary part of the research process. To help promote the new norm, I delineate (1) how each of the major constituencies of the research process (i.e., funders, journals, professional societies, departments, and individual researchers) can incentivize replications and promote the new norm and (2) any obstacles each constituency faces in supporting the new norm.
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 | 0.705 | 0.844 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".