Bringing home the bacon: Industry, employment, and income in boreal Canada
Bibliographic record
Abstract
Questions about the contribution of forestry to the socio-economic status of Canadian boreal communities have risen to the fore as debates have emerged about extending areas of protection in the region. Our previous research showed that boreal communities tend to be worse off socio-economically than other Canadian rural communities, and that labour income from the forest industry is relatively small. Because boreal development and protection initiatives are likely to be province-specific, this paper uses 2001 Statistics Canada data to examine the socio-economic status of boreal communities and the relationship between forest dependence and status—by province. We find a generally positive relationship between forest sector employment and employment income across the boreal region, but no such positive relationship between forest employment and unemployment rates, suggesting that the particular indicator chosen to represent wellbeing is a crucial consideration. Further, we see a great deal of inter-provincial variation in the relative importance of resource industries, suggesting the utility of province-specific and joint national initiatives. Finally, although the forest sector looms large in the collective psyche of the boreal region, we find diversified employment: other sectors (i.e., energy, agriculture, and hospitality) make a significant contribution to the economy of boreal communities. Key words: community status, well-being, forest dependence, forest policy, triad land management, boreal forest
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".