Report from the "What Is Open?" Workgroup
Bibliographic record
Abstract
The scholarly community’s current definition of “open” captures only some of the attributes of openness that exist across different publishing models and content types. Open is not an end in itself, but a means for achieving the most effective dissemination of scholarship and research. We suggest that the different attributes of open exist along a broad spectrum and propose an alternative way of describing and evaluating openness based on four attributes: discoverable, accessible, reusable, and transparent. These four attributes of openness, taken together, form the draft “DART Framework for Open Access.” This framework can be applied to both research artifacts as well as research processes. We welcome input from the broader scholarly community about this framework. OSI2016 workgroup questionThere is a broad difference of opinion among the many stakeholders in scholarly publishing about how to precisely define open access publishing. Are “open access” and “open data” what we mean by open? Does “open” mean anything else? Does it mean “to make available,” or “to make freely available in a particular format?” Is a clearer definition needed (or maybe just better education on the current definition)? Why or why not? At present, some stakeholders see public access as being an acceptable stopping point in the move toward open access. Others see “open” as requiring free and immediate access with articles being available in CC-BY format. The range of opinions between these extremes is vast. How should these differences be decided? Who should decide? Is it possible to make binding recommendations (and how)? Is consensus necessary? What are the consequences of the lack of consensus?
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.175 | 0.084 |
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".