“Bug Wood”: Climate Change, Mountain Pine Beetles and Risk in the Southeastern BRITISH COLUMBIA Logging Industry
Bibliographic record
Abstract
Abstract Purpose The purpose of this paper is to analyze how the culture in the logging industry in the East Kootenay/Columbia region in British Columbia, Canada, is changing as warm winters resulting from climate change drive expansion of a native tree-killing pest, the mountain pine beetle (Dendroctonus ponderosae). Methodology/approach The paper is derived from historical records and 11 months of ethnographic fieldwork conducted from July 2010 to May 2011. Findings This analysis found that the insect outbreaks are generating a heightened sense of economic and physical vulnerability in the logging industry, undermining previous assumptions of sufficiency and confidence. Research limitations/implications This paper presents results from a study of a specific region, and caution should be used when comparing these results with similar phenomena in other contexts. Social implications The forest industry is an important employer throughout the British Columbia interior; the cultural changes documented here indicate that climate change, manifested in insect outbreaks, is generating cultural dislocation that can have negative consequences beyond the immediate economic impacts. Originality/value This paper provides a detailed analysis of how an unanticipated consequence of climate change is driving adjustments in a subculture in a technologically advanced society.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".