Bibliographic record
Abstract
Purpose To provide an insider's review of the journal management and publishing software, Open Journal Systems (OJS), from the Public Knowledge Project, which the author directs at the University of British Columbia. Design/methodology/approach The paper outlines the history, development, and features of OJS, including some of the experimental aspects, as well as early research results and work underway, on which it is based. Findings OJS ( http://pkp.sfu.ca/ojs ) is an open source solution to managing and publishing scholarly journals online, which can reduce publishing costs compared to print and other traditional publishing processes. It is a highly flexible editor‐operated journal management and publishing system that can be downloaded for free and installed on a local web server. Originality/value OJS has been designed to reduce the time and energy devoted to the clerical and managerial tasks associated with editing a journal, while improving the record keeping and efficiency of editorial processes. It seeks to improve the scholarly and public quality of journal publishing through a number of innovations, from making journal policies more transparent to improving indexing.
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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.141 | 0.090 |
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".