Community Funding for Open Science Infrastructure: SCOSS 2 years in
Bibliographic record
Abstract
Germany, Austria, Switzerland and rest of Europe libraries have shown unprecedented commitment to facilitating open access to their institutions’ research outputs. The large scale transition to Open Access is accompanied by increasing policy commitments to Open Science (OS) across Europe. Moving towards ‘open’ means we are becoming more dependent on infrastructures that support us in depositing, managing, sharing and publishing research outputs openly. Much of the scholarly communications infrastructure run by not for-profits is free to libraries, which helps limit library costs for Open Science. However, this infrastructure has maintenance and development costs to contend with. Without funding, essential services that many are dependent upon are at risk; at risk of service degradation, reduced availability and of survival in some cases. This is infrastructure that we cannot do without. The Global Sustainability Coalition for Open Science Services (SCOSS) was established in 2017 to achieve this aim, ultimately to improve the financial position, resilience and sustainability of OS infrastructure services. SCOSS has helped raise 2.3 million Euros so far with France recently committing to this cause with almost 0.5 million. Pledges have come from most continents. Members include the Association of African Universities, the Association of Research Libraries (ARL), the Canadian Association of Research Libraries, the Council of Australian University Librarians, EIFL, LIBER, the Ministry of Higher Education, Research and Innovation, France and REDALYC, with SPARC Europe as the coordinator. SCOSS provides recommendations for funding to those interested in supporting important OS scholarly communications infrastructure, vetted by its members using robust evaluation processes. After a fruitful pilot SCOSS has received financial support from over 200 university libraries across the globe for SherpaRoMEO and DOAJ. We now look forward to presenting four new infrastructures for funding by the community that includes DOAB, OAPEN, the Public Knowledge Project (PKP) and Open Citations.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Open science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".