10 Years Experience with Pioneering Open Access Publishing in Health Informatics: The Journal of Medical Internet Research (JMIR)
Bibliographic record
Abstract
Peer-reviewed journals remain important vehicles for knowledge transfer and dissemination in health informatics, yet, their format, processes and business models are changing only slowly. Up to the end of last century, it was common for individual researchers and scientific organizations to leave the business of knowledge transfer to professional publishers, signing away their rights to the works in the process, which in turn impeded wider dissemination. Traditional medical informatics journals are poorly cited and the visibility and uptake of articles beyond the medical informatics community remain limited. In 1999, the Journal of Medical Internet Research (JMIR; http://www.jmir.org) was launched, featuring several innovations including 1) ownership and copyright retained by the authors, 2) electronic-only, "lean" non-for-profit publishing, 3) openly accessible articles with a reversed business model (author pays instead of reader pays), 4) technological innovations such as automatic XML tagging and reference checking, on-the-fly PDF generation from XML, etc., enabling wide distribution in various bibliographic and full-text databases. In the past 10 years, despite limited resources, the journal has emerged as a leading journal in health informatics, and is presently ranked the top journal in the medical informatics and health services research categories by impact factor. The paper summarizes some of the features of the Journal, and uses bibliometric and access data to compare the influence of the Journal on the discipline of medical informatics and other disciplines. While traditional medical informatics journals are primarily cited by other Medical Informatics journals (33%-46% of citations), JMIR papers are to a more often cited by "end-users" (policy, public health, clinical journals), which may be partly attributable to the "open access advantage".
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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".