Open Research Europe: A Catalyst towards Equitable Publishing
Bibliographic record
Abstract
The panel will explore the advancement of equitable publishing in Europe by focusing on the example of Open Research Europe (ORE), the open access publishing platform initiated by the European Commission. The discussion will be framed through the guiding vision and ambition to evolve ORE into a collectively supported publishing platform for the public good in collaboration with research funders and institutions, and underpinned by open source software. The case of ORE will be contextualized in the supportive European policy background as expressed through the 2023 European Council conclusions on equitable publishing, the new European Research area and the substantive efforts to support Diamond Open Access. The dialogue will investigate the potential of the ORE platform along with the roles, motivations, perspectives and expectations of organisations that participate in this initiative.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.084 | 0.010 |
| Open science | 0.028 | 0.040 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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; both teacher heads 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".