Benchmarking Trends In Aboriginal Forestry
Bibliographic record
Abstract
The forest has long been a central component of the culture of Aboriginal Canadians, and opportunities for economic development in Aboriginal forestry are emerging in several areas. The amount of forest assets managed by Aboriginal peoples has been increasing as well as the types of business relationships Aboriginal peoples are engaged in are expanding. However, the development of Aboriginal capacity in the forest sector is varied. The educational and skill-levels of Aboriginal workers in forestry is improving, but the average age of the Aboriginal workforce has steadily increased, which reflects a rapidly aging underlying demographic. While the median total income for Aboriginal workers in the forest sector has increased, the number of Aboriginal workers in the forest sector has steadily declined. Further, average income drastically varies depending on whether an individual is on or off-reserve. The earnings of off-reserve Aboriginal forestry workers are very close to those of their non-Aboriginal counterparts, while on-reserve forestry workers are earning less than half of what non-Aboriginals make. Benchmarking these trends is important as it facilitates the continued tracking of the role of Aboriginal peoples in the forest sector as it changes and new areas of opportunity develop.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".