Expanding perspective on open science: communities, cultures and diversity in concepts and practices
Bibliographic record
Abstract
Twenty-one years ago, the term ‘electronic publishing’ promised all manner of potential that the Web and network technologies could bring to scholarly communication, scientific research and technical innovation. Over the last two decades, tremendous developments have indeed taken place across all of these domains. One of the most important of these has been Open Science; perhaps the most widely discussed topic in research communications today. \n \nThis book presents the proceedings of Elpub 2017, the 21st edition of the International Conference on Electronic Publishing, held in Limassol, Cyprus, in June 2017. Continuing the tradition of bringing together academics, publishers, lecturers, librarians, developers, entrepreneurs, users and all other stakeholders interested in the issues surrounding electronic publishing, this edition of the conference focuses on Open Science, and the 27 research and practitioner papers and 1 poster included here reflect the results and ideas of researchers and practitioners with diverse backgrounds from all around the world with regard to this important subject. \n \nIntended to generate discussion and debate on the potential and limitations of openness, the book addresses the current challenges and opportunities in the ecosystem of Open Science, and explores how to move forward in developing an inclusive system that will work for a much broader range of participants. It will be of interest to all those concerned with electronic publishing, and Open Science in particular.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.079 |
| Scholarly communication | 0.032 | 0.046 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| 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".