Funding Systems Change : Challenges and Opportunities
Bibliographic record
Abstract
Many foundations across the world are increasingly interested in systems change. In the framewor of the SIX Funders Node's activities, 22 foundations and systems change experts have been brought together in September 2016 for a retreat on Wasan Island, in the Muskoka region of Ontario, Canada. Despite coming from different countries and focus areas, the participants were united in their curiosity and desire to create systemic impact. While the retreat drew on global thinking and case studies, it was rooted in practice and provided a unique and focused opportunity for foundations working on systems change, or moving towards systems chance. The purpose of this report is to highlight the learning from that retreat; and to help nurture the emerging community of foundations working in systems change by codifying and sharing examples and practices from the pioneers and early adopters of this approach.
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 | 0.083 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".