Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the potential of Wikipedia as a venue for academic publishing. Design/methodology/approach By looking at other sources and studying Wikipedia structures, the paper compares the processes of publishing a peer‐reviewed article in Wikipedia and the open access journal model, discusses the advantages and challenges of adopting Wikipedia in academic publishing, and provides suggestions on how to address the challenges. Findings Compared to an open access journal model, Wikipedia has several advantages for academic publishing: it is less expensive, quicker, more widely read, and offers a wider variety of articles. There are also several major challenges in adopting Wikipedia in the academic community: the web site structure is not well suited to academic publications; the site is not integrated with common academic search engines such as Google Scholar or with university libraries; and there are concerns among some members of the academic community about the site's credibility and impact in academia. Originality/value This paper promotes a fundamental idea for adjusting methods of creating and disseminating academic knowledge. It is a valuable resource for those interested in academic innovation, for research librarians, and for the academic community in general. This topic has not been sufficiently addressed in the literature.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.031 | 0.033 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".