Envisioning the scientific paper of the future
Bibliographic record
Abstract
Consider for a moment the rate of advancement in the scientific understanding of DNA. It is formidable; from Fredrich Miescher’s nuclein extraction in the 1860s to Rosalind Franklin’s double helix X-ray in the 1950s to revolutionary next-generation sequencing in the late 2000s. Now consider the scientific paper, the medium used to describe and publish these advances. How is the scientific paper advancing to meet the needs of those who generate and use scientific information? We review four essential qualities for the scientific paper of the future: ( i) a robust source of trustworthy information that remains peer reviewed and is ( ii) communicated to diverse users in diverse ways, ( iii) open access, and ( iv) has a measurable impact beyond Impact Factor. Since its inception, scientific literature has proliferated. We discuss the continuation and expansion of practices already in place including: freely accessible data and analytical code, living research and reviews, changes to peer review to improve representation of under-represented groups, plain language summaries, preprint servers, evidence-informed decision-making, and altmetrics.
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 | 0.213 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.064 | 0.079 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".