The Public Knowledge Project: Reflections and Directions After Its First Two Decades
Bibliographic record
Abstract
As the Public Knowledge Project (PKP) enters its third decade, it faces the responsibilities of supporting the more than 10,000 journals using its software and are dependent on PKP continuing to develop the code. In the fall of 2017, PKP, with the support of the Arnold Foundation, contracted the consulting services of BlueSky to Blueprint, with its principal Nancy Maron embarking on an exploration of PKP’s standing and prospects among a sample of those in-volved in scholarly publishing, inclu-ding current, former, and potential users of its software (Maron 2018). This paper presents BlueSky’s findings and PKP’s responses in what may serve as a lesson on the maturing of, and challenges faced by, an open source software project seeking to sustain in-creased global access to research and scholarship.
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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 | 0.078 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".