Open and reproducible science practices in psychoneuroendocrinology: opportunities to foster scientific progress
Bibliographic record
Abstract
This perspective article was written by invitation of the editors in chief as a summary and extension of the symposium entitled Psychoneuroendocrine Research in the Era of the Replication Crisis which was held at the virtual meeting of the International Society of Psychoneuroendocrinology 2021. It highlights the opportunities presented by the application of open and reproducible scientific practices in psychoneuroendocrinology (PNE), an interdisciplinary field at the intersection of psychology, endocrinology, immunology, neurology, and psychiatry. It conveys an introduction to the topics preregistration, registered reports, quantifying the impact of equally-well justifiable analysis decisions, and open data and scripts, while emphasizing ‘selfish’ reasons to adopt such practices as individual researcher. Complementary to the call for adoption of open science practices, we highlight the need for methodological best practice guidelines in the field of PNE, which could further contribute to enhancing replicability of results. We propose concrete steps for future actions and provide links to additional resources for those interested in adopting open and reproducible science practices in future studies.
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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 | 0.702 | 0.814 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.044 | 0.043 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 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".