A Review of the Potential Impacts of the Métis Human Resources Development Agreements in Canada
Bibliographic record
Abstract
Since 1999, thousands of Métis have received training and found employment through Métis Human Resources Development Agreements (MHRDAs). We estimate MHRDA activities’ annual fiscal impact, which includes higher tax revenue,lower government transfers, mostly in the form of EI and social assistance, and lower health expenditures. Based on results from the 2007-2008 fiscal year, we estimate the annual fiscal impact of one year of activity to be between $4.2 and $47.9 million, with a higher probability associated to the lower-bound estimate than the upper-bound estimate.On a long-term basis, the discounted fiscal benefits outweigh program costs (about $49 million for one year of activity) in all cases but the one based on a the lower-bound estimate and highest discount rate. Our middle-bound estimate suggests annual fiscal benefits of $8.5 million, with long-term benefits reaching $103 million. Given that benefits from Métis training and employment encompass more than what is captured in this analysis, the return from the MHRDA for Canadian society appears to be well worth the investment.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".