Making the Global Open Research Commons Truly Global: A report from the Lorentz Workshop, July 21-25 2025
Bibliographic record
Abstract
This report describes the conduct and outcomes of a Lorentz workshop held on July 21-25 2025. The workshop was organised by a subset of the chairs of the Research Data Alliance (RDA) Global Open Research Commons (GORC) Interest Group and International Implementations Working Group, augmented and reinforced by staff from SURF. It was held as a Lorentz Centre workshop to embracer the advantages of this specific location for an in-depth and broad ranging collaborative exploration of a range of issues. The specific setting of a Lorentz Workshop gave space and time to re-inspect past debates, and to share experiences to implement the model in practice. The workshop discussed a wide range of issues, which are documented in this report and its appendices. The overwhelming message from those who attended the workshop is that the GORC International Model is well-constructed, useful and being used. The structure of the model draws on existing good practice as well as fundamental information science principles, and has been carefully refined through community review. The model is applicable to a wide range of settings. While it might need further fine-tuning as with any model, it is already an excellent epistemic framework to enable individuals and organisations to reflect theoretically and practically about their activities and needs in the realm of digital research infrastructure. And, the model is being used in practice for a variety of purposes, including interoperability between commons and internally within organisations. It is already having an impact by shaping national and pan-national digital research infrastructures. NOTE: This version contains the same text as version 1, but (thanks to generous support from SURF) has been professionally laid out.
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 | 0.047 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".