Forest Sector Dependence and Community Well‐being: A Structural Equation Model for New Brunswick and British Columbia*
Bibliographic record
Abstract
AbstractRural sociologists have a lengthy history of examining the relationship between natural resource dependence and community well‐being. This paper contributes to the understanding of this relationship in several ways. First, census data were used to describe forest sector dependence in two Canadian provinces where levels of dependence were much higher than those commonly found in the United States. Second, instead of linear regression analysis, a structural equation model was used to provide estimates for three indicators of well‐being (income, poverty, and inmigration) within a single model and then the model was tested for overall suitability. Using market segmentation theory, this paper shows that forest dependence and well‐being in New Brunswick is more consistent with many places in the United States where the pulp and paper industry alone is positively associated with well‐being indicators. In contrast, pulp and paper, logging, and lumber sectors in British Columbia are positively associated with well‐being. The model also reveals less transience in forestry towns than was previously assumed. These findings are discussed along with estimated effects between indictors of community well‐being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".