A culturally appropriate approach to civic engagement: Addressing forestry and cumulative social impacts in southwest Yukon
Bibliographic record
Abstract
This article reports on an experimental civic engagement approach to link community observed cumulative effects ofnumerous local events and periods of resource development to indicators for sustainable forest and land management forthe future. We describe a process where the interview findings with 28 key aboriginal and non-aboriginal informants inthe Champagne Aishihik First Nations’ (CAFN) Traditional Territory were summarized into key themes by researchersin a community workshop to elicit a selection of social indicators for future cumulative effects assessments. Theseresponses were visions for the future based on a great deal of experiential learning that interviewees identified—part andparcel of any betterment to the community as new developments unfold. Themes such as “social healing” were furtherbroken into indicators such as “community support systems” and then further broken into local measures, such as “thepresence of, and access to, a youth centre, youth programs, and youth centres”. The local historical approach to cumulativeeffects assessment helps us not only understand more about forestry, but more about the broader connectionsbetween community members and leaders, forestry and other resource developments, and lessons people have learnedfrom the past and visions for the future.Key words: civic engagement, cumulative social impacts, social indicators, sustainable forest management, NorthernCanada
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".