Thinking Big: Community Philanthropy and Management of Large-Scale Assets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents three case studies — from Ghana, the U.S., and Canada — to examine how community philanthropy might scale up to support community asset management and increase the power of communities to determine their own development with much greater and more complex financial investments. Community philanthropy institutions have become increasingly popular — especially in the Global South, where they serve to harness local assets, cultivate local capacities, and build trust among diverse stakeholders. Although bilateral donors and other international development funders are beginning to recognize the power of these local organizations, they are usually considered small-scale actors. As resource extraction continues to reach into remote areas and other large-scale industries (e.g. solar energy, agroforestry) grow, pressure on resources and the rights of communities will intensify. This article illustrates the agility, responsiveness, and effectiveness of the Newmont-Ahafo Development Foundation, the Cherokee Preservation Foundation, and the Clayoquot Biosphere Trust, and presents a case that, despite organizational challenges, community philanthropy has demonstrated the power to promote community self-determination, democratic decision-making, and more sustainable results from development projects.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it