Bibliographic record
Abstract
Close to the midpoint of the 2030 Agenda for Sustainable Development, local communities and other sectors are becoming more aware of their role in the achievement of the SDGs and have taken the challenge to localize, implement, and report on their actions and progress (IISD, 2022). Within the civil society sector, more evidence and research has been developed on the role of philanthropy in localizing and achieving the SDGs. In this regard, Community Foundations (CFs) and Community Foundations Support Organizations (CFSOs) mostly in Europe and North America (Canada and the US, specifically) have developed a set of tools and frameworks to ensure that the SDGs relate to local realities (DiSabato, 2017; Leone & LeSage, 2021; UKCF, 2021). Meanwhile, CFs and CFSOs in Latin America and the Caribbean are leading diverse paths and efforts toward sustainability within the SDG framework that need to be showcased. This paper explores the relevance of framing the SDGs into the work of community foundations and compiles localization approaches and strategies being used by CFs around the world, providing examples with a focus in Latin America and potential next steps for the second half of the journey toward 2030.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.041 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".