The political ecology of local environmental narratives: power, knowledge, and mountain caribou conservation
Bibliographic record
Abstract
Political ecology seeks to address notable weaknesses in the social sciences that consider how human society and the environment shape each other over time. Considering questions of ideology and scientific discourse, power and knowledge, and issues of conservation and environmental history, political ecology offers an alternative to technocratic approaches to policy prescriptions and environmental assessment. Integrating these insights into the science-policy interface is crucial for discerning and articulating the role of local resource users in environmental conservation. This paper applies political ecology to addresses a gap in the literature that exists at the interface of narratives of local environmental change and local ecological knowledge and doing so builds a nuanced critique of the rationality of local ecological knowledge. The ways that we view nature and generate, interpret, communicate, and understand the "science" of environmental problems is deeply embedded in particular economic, political, and ecological contexts. In interior British Columbia, Canada, these dynamics unfold in one of the most rigorously documented examples of the negative effect of anthropogenic disturbance on an endangered species – declining mountain caribou population. Science notwithstanding, resource users tell narratives of population decline that clearly reflect historical regularities deeply embedded in particular economic, political, and ideological constructions situated in local practices. This research assesses these narratives, discusses the implications, and explores pathways for integrating local knowledge and narratives into conservation science and policy. A more informed understanding of the subjectivities and rationalities of local knowledges can and should inform conservation science and policy.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".