Territorial Formula Financing in the Context of First Nations Governments
Bibliographic record
Abstract
Problem statementThere are at least four deficiencies in the current approach to fiscal transfers between the federal government and First Nations.First, funding is insufficient for First Nations to deliver effective programs and services that will improve outcomes on-reserve and in communities.Second, funding is short-term, and therefore is neither predictable nor sustainable.Third, funding does not allow for growth to address rising costs in service delivery, inflation, and increases in governance capacity.Fourth, the current system of separate grants, each with their own reporting and accountability requirements, creates heavy and unnecessary administrative burdens for First Nations.Provided it is applied appropriately, Territorial Formula Financing (TFF) addresses these problems and provides a financial framework for autonomy and a nation-to-nation partnership.The following paper analyzes the TFF as a new model for First Nations and offers specific recommendations for how this model could be modified and applied. Key Messages• Current fiscal transfers to First Nations are inadequate.Funding is insufficient, unfairly capped and unpredictable.Administrative burdens are also excessive and onerous;• The TFF is grounded in principles that address current problems, and if carefully designed, a modified version could be applied to the First Nations context;• A new formula should aim for reasonably comparable outcomes, should not be capped at 2 per cent, and should be introduced in a full and timely manner for First Nations with the capacity, but gradually in concert with capacity-building initiatives for those that do not;• The "service population" variable of an escalator should be determined carefully;• It is imperative to measure expenditure needs to emphasize macro comparability; and• Timing and sequencing must be carefully considered.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.000 |
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".