Renewing Funding Relationships: Certifying First Nations Social Service Administrators
Bibliographic record
Abstract
Governments have been key funders of both social economy (SE) organizations and First Nation communities, yet the relationships between them have not necessarily been easy to negotiate. Challenges abound for government funders and SE and First Nation recipients in building respectful relations. Some of the key factors contributing to the challenges include: • Increased demand for accountability in government spending • Differing perceptions between the parties as to the appropriate roles of each • SE organizations and First Nations may perceive government-determined funding eligibility criteria and/or priorities as obstacles to responding to community need • Fear that difficulties in program administration may result in loss of funding or cooptation by funders of programs away from community need In this article, using a case study approach, we argue that a renewed relationship between government funders and First Nations and SE organizations can be based on an improved understanding of one another’s perspective.1 Without such a renewal, vital programs and services shall be left floundering without the crucial input of community-based knowledge and needs assessments. We will conclude this article with a number of recommended directions for the development of such a renewal.
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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 | 0.068 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.019 | 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".