Consistent and Precise Description of Research Outputs Could Improve Implementation of Open Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2013, the Center for Open Science proposed that journal articles be awarded “badges” for engaging in open-science practices, including preregistration. In 2015, the Transparency and Openness Promotion (TOP) guidelines (TOP 2015) promoted preregistration of studies and analysis plans. Since then, the term “preregistration” has been used to describe different research outputs created at different times—sometimes, but not always, including study registration. Following a review of evidence about TOP 2015 implementation, including evidence that adherence could not be rated reliably, the TOP Guidelines Advisory Board updated these guidelines (TOP 2025). The TOP 2025 guidelines no longer use the term “preregistration.” Instead, TOP 2025 disambiguates specific research outputs, such as registrations, study protocols, analysis plans, code, and other research materials. TOP 2025 also explains that researchers should describe the time at which outputs are created and shared in relation to key study activities. In this article, we explain why adopting the terminology used in TOP 2025 and describing the times at which specific research outputs are created and shared will enhance understanding and support better implementation and reporting of open science.
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.
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 | 0.447 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it