Community funding for Open Science infrastructure: SCOSS 2 years in
Bibliographic record
Abstract
With a huge Open Science (OS) policy momentum, libraries are increasingly facilitating access to their institutions’ research outputs. We are becoming more dependent on an infrastructure that supports us in depositing, managing, sharing and publishing our research outputs openly. Without funding, essential services that many are dependent upon are at risk of service degradation, reduced availability and of survival in some cases. As a library community we can help this infrastructure remain in our hands and not for-profit by financially support it through crowd-funding and by helping govern that work. 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. It has members from most continents, including the Association of African Universities, the Association of Research Libraries (ARL), the Canadian Association of Research Libraries, the Council of the Australian University Librarians, EIFL, LIBER, the Ministry of Higher Education, Research and Innovation, France, and REDALYC, working 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. After a fruitful pilot raising over 2.3 million euros, SCOSS has robust processes and governance in place to continue to recommend important infrastructure for funding in 2020 and beyond. We look forward to discussing how such crowd-funding works, how you might contribute, why, or why not and how the community can have a say in the governance of OS community-funded infrastructure. This poster will provide an introduction to SCOSS and its purpose and processes. The poster will share insights on the evaluation process, outcomes from the first (pilot) and the second round and will showcase our infrastructures that we recommend the community financially supports.
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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.041 | 0.013 |
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".