A Duty to Describe
Bibliographic record
Abstract
Although many researchers have discussed replication as a means to facilitate self-correcting science, in this article, we identify meta-analyses and evaluating the validity of correlational and causal inferences as additional processes crucial to self-correction. We argue that researchers have a duty to describe sampling decisions they make; without such descriptions, self-correction becomes difficult, if not impossible. We developed the Replicability and Meta-Analytic Suitability Inventory (RAMSI) to evaluate the descriptive adequacy of a sample of studies taken from current psychological literature. Authors described only about 30% of the sampling decisions necessary for self-correcting science. We suggest that a modified RAMSI can be used by authors to guide their written reports and by reviewers to inform editorial recommendations. Finally, we claim that when researchers do not describe their sampling decisions, both readers and reviewers may assume that those decisions do not matter to the outcome of the study, do not affect inferences made from the research findings, do not inhibit inclusion in meta-analyses, and do not inhibit replicability of the study. If these assumptions are in error, as they often are, and the neglected decisions are relevant, then the neglect may create a good deal of mischief in the field.
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 | 0.459 | 0.756 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".