Indicators of forest-dependent community sustainability: The evolution of research
Bibliographic record
Abstract
This paper describes the evolution of research on socio-economic indicators of community sustainability in several Canadian Model Forest locations since 1994. In the Foothills and Western Newfoundland Model Forests, we employed an "expert-driven" approach to indicator selection and reporting. We used census data to document change over time on key community profile variables such as age, employment, income, population mobility, education attainment, poverty, and real estate values. Objective measures of these variables were supplemented with personal interviews in order to construct a more dynamic picture of community well-being. The early work of our group focused primarily on "profile" indicatorsessentially static, descriptive indicators that allow one to create a snapshot of a community in time. Work is currently underway on the next generation of socio-economic indicators we describe as "process" indicators. Process indicators deal more with causal affects than outcomes. They include things like sense of place or attachment to place (which has implications for population mobility and education attainment). Process indicators also include variables such as leadership, volunteerism, entrepreneurship, and social cohesionall of which we are attempting to include in a combined measure of community capacity. Key words: social indicators, community sustainability, model forest, forest-dependent communities, SIMFOR
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".