Indigenous Peoples Resilience Fund: Building Infrastructure for Indigenous Philanthropy
Bibliographic record
Abstract
This report presents a brief overview of the Indigenous Peoples Resilience Fund (IPRF): a multi-funder, Indigenous-led initiative established to support Indigenous communities across Canada as they respond to the current health crisis. In doing so, IPRF also contributes to the construction of an Indigenous philanthropic infrastructure in Canada.The report is based on several conversations with key stakeholders in the process of establishing the IPRF. Two in-depth semi-structured interviews with individuals that started the initiative: Bruce Lawson, CEO of the Counselling Foundation of Canada; Victoria McKenzie Grant, Teme-Augama Anishnabai Kway (Woman of the Deep Water People) and Wanda Brascoupé, Kanien'keha, Skarù r?', Anishinabe, as representatives of the Indigenous Peoples Resilience Fund. Along with these conversations, the analysis also draws on conversations with Andrew Chunilall, CEO of Community Foundations Canada (the host partner of IPRF), and Jennifer Brennan, Head of Canada Programs at the Mastercard Foundation, which participated in initial funder consultations that preceded the establishment of the fund. Information on IPRF objectives, priorities, and future steps come from a draft version of the IPRF founding document, which was made available by the three key informants. The interviews were conducted in the first half of May 2020.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".