Philanthropy as a \nVehicle for Regional \nDevelopment
Bibliographic record
Abstract
Philanthropy, especially through the community foundations, is a powerful catalyst for strengthening communities in Canada. Community foundations and their board members are interested and engaged in finding ways to make their communities a more vital place to live, work, and play. Community foundations strive to build stronger communities through philanthropic leadership. The goal of this project is to examine how community foundations can influence and participate in regional development in Newfoundland and Labrador. Regional development, for the purposes of this proposal, is conceived in a holistic manner encompassing social, cultural, humanitarian, community development, and capacity building. \n \nIn addressing this goal, the project will achieve the following five objectives: \n(a) to create an overview portrait of charitable giving in Newfoundland and Labrador, \n(b) to situate the challenges and opportunities of the Community Foundation of Newfoundland and Labrador in relation to other Atlantic Canada community foundations, \n(c) to collect perspectives on the community foundation model in Newfoundland and Labrador from community residents and estate planners, \n(d) to identify potential roles, actions, and activities of the community foundation model can facilitate regional development, and \n(e) to initiate dialogue and share knowledge across actors from academia, government, private sector, and community/regional development practitioners.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".