Celebrating 20 Years of Open Access and Innovation at JMIR Publications
Bibliographic record
Abstract
In this 20th anniversary theme issue, we are celebrating how JMIR Publications, an innovative publisher deeply rooted in academia and created by scientists for scientists, pioneered the open access model, is advancing digital health research, is disrupting the scholarly publishing world, and is helping to empower patients. All this has been made possible by the disintermediating power of the internet. And we are not done innovating: Our new series of "superjournals," called JMIRx, will provide a glimpse into what we see as the future and end goal in scholarly publishing: open science. In this model, the vast majority of papers will be published on preprint servers first, with "overlay" journals then competing to peer review and publish peer-reviewed "versions of record" of the best papers.
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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".