Knowing caribou : Inuit, ecological science and traditional ecological knowledge in the Canadian North
Bibliographic record
Abstract
I document the knowledge forms of three distinct constituencies operating in the community of Cambridge Bay in the Canadian Arctic: its Inuit inhabitants, visiting ecological scientists and researchers documenting ‘traditional ecological knowledge’ (TEK). I explore the relationships that develop between them during attempts to understand the Dolphin-Union caribou herd, which inhabits Victoria Island and the adjacent mainland. I argue that there are fundamental differences between what ecologists, TEK researchers and Inuit understand to constitute knowledge and that these currently impair efforts at collaboration. I show what these differences are and suggest how they might be transcended. I pursue these themes through an ethnographic study of the ways in which relationships between risk and knowledge are played out in practice among Inuit hunters, scientific ecologists and TEK researchers. Following the work of Beck (1986), I argue that modern conceptions of risk fundamentally underpin the structure and focus of western science, giving priority to knowledge that will aid prediction, control and management. Meanwhile Inuit hunters, operating largely outside of modernity’s preoccupation with risk, find these concerns rather unnecessary. I move on to address the underlying epistemological similarity between Inuit knowledge and scientific ecological knowledge. This similarity is often obscured by representations of scientific knowledge as comprising an accumulation of discrete, prepositional, impersonal products. An ethnographic account of the processes that contribute to the formation of ecological science and Inuit knowledge shows that in practice, both rest on learning by experience within a process of engaged activity. Focusing on the similarities at this fundamental level I suggest how scientific and Inuit knowledge may be integrated in a way that remains faithful to Inuit understandings of what it is to know caribou.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.033 | 0.026 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".