First Peoples Economic Growth Fund: A Case Study of a Successful Aboriginal Financial Institution
Bibliographic record
Abstract
Start-Up Challenges Having identified lack of access to capital as a primary challenge, the AMC and the Province formed a group "to work on development of some sort of organization that would address that need" (Cramer, personal communication, November 6, 2020) and ultimately selected the AFI framework as the appropriate vehicle for change. Mr. Cramer described the makeup of the board as "a business board and not a political board, so it's made up of people with different business skills and when you bring them all together it's a very strong business and economic development board" (personal communication, November 6, 2020). The success of this method is compounded by the fact that the board is volunteer-based, which "for a board of directors of a financial organization [is] extremely rare, and also an extremely positive and strong part of our governance structure" (Cramer, personal communication, November 6, 2020). In what ways do AFIs engage with these challenges? * What are the benefits/limitations of attracting capital from each of the following: * Municipal/Provincial/Federal Colonial governments. * Indigenous Communities/Tribal Councils/Multi-community organizations. * Private Financial Institutions/Commercial Banks/Venture Capital * Are there examples of AFIs in Manitoba or Canada pursuing policies different from those of the First Peoples Economic Growth Fund (FPEGF)?
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".