Predicting and reasoning about replicability using structured groups
Bibliographic record
Abstract
This paper explores judgements about the replicability of social and behavioural sciences research, and what drives those judgements. Using a mixed methods approach, it draws on qualitative and quantitative data elicited using a structured iterative approach for eliciting judgements from groups, called the IDEA protocol (‘Investigate’, ‘Discuss’, ‘Estimate’ and ‘Aggregate’). Five groups of five people separately assessed the replicability of 25 ‘known-outcome’ claims. That is, social and behavioural science claims that have already been subject to at least one replication study. Specifically, participants assessed the probability that each of the 25 research claims will replicate (i.e. a replication study would find a statistically significant result in the same direction as the original study). In addition to their quantitative judgements, participants also outlined the reasoning behind their judgements. To start, we quantitatively analysed some possible correlates of predictive accuracy, such as self-rated understanding and expertise in assessing each claim, and updating of judgements after feedback and discussion. Then we qualitatively analysed the reasoning data (i.e., the comments and justifications people provided for their judgements) to explore the cues and heuristics used, and features of group discussion that accompanied more and less accurate judgements.
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 | 0.785 | 0.936 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".