Bibliographic record
Abstract
This article presents the rationale, common practices, challenges, and some personal anecdotes from a journal editor on the production, use, and re-use of peer-reviewed, scholarly articles as open educational resources (OER). The scholarly and professional discourse related to open educational resources has largely focused on open learning objects, courseware, and textbooks. However, especially in graduate education, articles published in scholarly journals are often a major component of the course content in formal education. In addition, open access journal articles are critical to expanding access to knowledge by scholars in the developing world and in fostering citizen science, by which everyone has access to the latest academic information and research results. In this article, I highlight some of the challenges, economic models, and evidence for quality of open access journal content and look at new affordances provided by the Net for enhanced functionality, access, and distribution. In the seventeen years since I graduated with a doctorate degree, the climate and acceptance of open access publishing has almost reversed itself. I recall a conversation with my PhD supervisor in which he argued that publishing online was not a viable option as the product would not have permanency, scholarly recognition, or the prestige of a paper publication. His comments reflect the confusion between online resources and those described as open access, but as well illustrate the change in academic acceptance and use of open access products during the past decade. The evolution from paper to online production and consumption is a disruptive technology in which much lower cost and increased accessibility of online work opens the product to a completely new group of potential users. In the case of OER these consumers are primarily students, but certainly access to scholars from all parts of the globe and the availability to support citizen science (Silvertown, 2009) should not be underestimated.
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 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.043 | 0.036 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.031 |
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".