Make researchers revisit past publications to improve reproducibility
Bibliographic record
Abstract
Scientific irreproducibility is a major issue that has recently increased attention from publishers, authors, funders and other players in the scientific arena. Published literature suggests that 50-80% of all science performed is irreproducible. While various solutions to this problem have been proposed, none of them are quick and/or cheap. Here, we propose one way of reducing scientific irreproducibility by asking authors to revisit their previous publications and provide a commentary after five years. We believe that this measure will alert authors not to over sell their results and will help with better planning and execution of their experiments. We invite scientific journals to adapt this proposal immediately as a prerequisite for publishing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.661 | 0.689 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.015 | 0.000 |
| Open science | 0.025 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.067 |
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; both teacher heads 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".