Continuous Tools for Scientific Publishing
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
Advances in technology for data workflows have increased the speed and scope of scientific discovery, however, scientific dialogue still uses outdated technology for communicating and sharing knowledge. The widespread reliance on static PDF formats for research papers starkly contrasts with the complex, data-driven and increasingly computational nature of modern science. This gap, which is especially evident in the computational sciences, impedes the speed of research dissemination, reuse, and uptake. We require new mediums to compose ideas and ways to share research findings iteratively, as early as possible and connected directly to software and data. In this paper we discuss two tools for scientific authoring and publishing, MyST Markdown and Curvenote, and illustrate examples of improving metadata, reimagining the reading experience, including computational content, and transforming publishing practices for individuals and societies through automation and continuous practices. We focus on the unique aspects of the tools, which enable computational and interactive content, publishing and sharing continuously through automated checking and typesetting, and provide case studies from individuals to societies who have adopted these tools.
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 | 0.076 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.031 | 0.201 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.074 | 0.011 |
| Open science | 0.010 | 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