Indigenous Peoples Resilience Fund: Building Infrastructure for Indigenous Philanthropy
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 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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it