Discussing open access. The new publication and public exposure model as an open access version
Bibliographic record
Abstract
The article of mosaic structure comprises the materials of the panel discussion “Collaboration & community. The transition to open access” held by Copyright Clearance Center (USA) within the framework London Book Fair in April, 2018. These materials (arguments presented by four discussion participants, experts in scholarly publishing: a publisher of Cambridge University Press, Cambridge University librarian, expert of Royal Society of Chemistry, and Executive Director of Knowledge Unlatched, a crowdfunding platform), and the introduction speech by Christopher Kenneally, discussion moderator, were published on the discussion date and distributed to the participants. By courtesy of Christopher Kenneally and Alastair Horn in charge of the Academic Bulletin where the prematerials were first published, we are including the original English-language text, the translation and the author’s summary and arguments on expanding publication formats, e. g. outspreading of panel sessions at conferences. The article is aimed at two goals: to introduce readers to several issues of the open access system voiced at the discussion panel, and to analyze in detail the methods of audience communication (the technologies based on open access principles). The selection of materials, permission for translation into Russian (and the translation) as well as the permission for reprinting the original are accomplished by Andrey I. Zemskov, RNPLS&T leading researcher, who attended the discussion panel. He is also the author of the summary review on the new publication and public exposure model as an open access version.
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 | 0.056 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.049 | 0.067 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".