Open Access to Research. Changing Researcher Behavior Through University and Funder Mandates.
Bibliographic record
Abstract
The primary target of the worldwide Open Access initiative is the 2.5 million articles published every year in the planet's 25,000 peer-reviewed research journals across all scholarly and scientific fields. Without exception, every one of these articles is an author give-away, written, not for royalty income, but solely to be used, applied and built upon by other researchers. The optimal and inevitable solution for this give-away research is that it should be made freely accessible to all its would-be users online and not only to those whose institutions can afford subscription access to the journal in which it happens to be published. Yet this optimal and inevitable solution, already fully within the reach of the global research community for at least two decades now, has been taking a remarkably long time to be grasped. The problem is not particularly an instance of "eDemocracy" one way or the other; it is an instance of inaction because of widespread misconceptions (reminiscent of Zeno's Paradox). The solution is for the world's research institutions and funders to (1) extend their existing "publish or perish" mandates so as to (2) require their employees and fundees to maximize the usage and impact of the research they are employed and funded to conduct and publish by (3) depositing their final drafts in their Open Access (OA) Institutional Repositories immediately upon acceptance for publication in order to (4) make their findings freely accessible to all their potential users webwide. OA metrics can then be used to measure and reward research progress and impact; and multiple layers of links, tags, commentary and discussion can be built upon and integrated with the primary research.
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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 | 0.201 | 0.437 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".