Protocols and Challenges to the Creation of a Cross-Disciplinary Journal
Bibliographic record
Abstract
In 2006, the Online Journal of Rural Research and Policy (OJRRP) was launched. The publication is an example of the ability of academia to create narrowly defined scholarly journals aimed at a small, targeted readership while relying on a meagre budget. This article discusses the factors that fostered the creation of hundreds of online-only journals, as well as providing a case study of the creation of OJRRP and the long-term implications of online cross-disciplinary publications. Areas covered include sponsorship, editorial board, editorial staff, software, link rot, code, promotional activities, tracking and supporting usage, and, perhaps most importantly, long-term sustainability. The OJRRP experience is presented along with lessons learned in each area.
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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 | 0.488 | 0.474 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.048 | 0.035 |
| Open science | 0.009 | 0.029 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".