From climate to caribou: How manufactured uncertainty is affecting wildlife management
Bibliographic record
Abstract
ABSTRACT Over the past decade, declines of Canadian populations of boreal caribou ( Rangifer tarandus caribou ) have received considerable attention from scientists, government agencies, environmental nongovernmental organizations, Indigenous communities, and the forest industry. Boreal caribou (also known as boreal woodland caribou) was listed as a threatened species in Canada when the Species at Risk Act came into force in June 2003. Many boreal caribou populations have been shown to be decreasing, in some cases precipitously, and empirical evidence from adult survival and calf recruitment surveys indicates that the cumulative effect of habitat disturbance, including that which results from industrial development, is a key driver in the decline. Yet, as scientific understanding of the decline has become clearer, and agreement among scientists and governments about habitat management requirements has increased, campaigns of denial have intensified in the public sphere. In this paper, we examine parallels with climate change rhetoric prolific in the 2000s and show that willful ignorance disguised as skepticism has resulted in public uncertainty despite robust scientific evidence. We show how these strategies of manufactured uncertainty used in climate change denial campaigns have seeped into wildlife management debates, with pernicious results. In this case, it has successfully delayed efforts to effectively address the decline of boreal caribou, which is protected under federal, provincial, and territorial legislation, and inhibited meaningful dialogue about socially acceptable conservation solutions. © 2018 The Wildlife 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".