Open science: My insights into data sharing, preregistration, and replication.
Bibliographic record
Abstract
After a decade of implementing open science practices as a principal investigator, mentor, data repository founder, and editor-in-chief, I have learned that the question is not whether researchers should adopt these practices but how to adapt them meaningfully. This commentary, based on a talk given at the 2024 Canadian Society for Brain, Behaviour, and Cognitive Science conference, argues for two key principles: First, open science implementation must be context-dependent rather than one-size-fits-all, and second, practical research realities require flexible approaches to idealized policies. Through personal examples, from my evolution with preregistration from "recipe" to "guide" during COVID-19 research to challenges with Registered Reports using existing data sets, I show how open science practices work best when researchers approach them as evolving tools rather than rigid rules. I also discuss field-specific differences in open science uptake between psychology and education and the importance of equity considerations in implementation. The commentary concludes with concrete recommendations for researchers and journals, emphasizing that sustainable open science requires meeting researchers where they are while maintaining transparency and scientific rigour. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchOpen science Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.391 | 0.618 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.019 | 0.085 |
| Scholarly communication | 0.029 | 0.037 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.025 | 0.029 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".