Knowledge Production and Policy in the Arctic: The Political Ecology of the Thick-billed Murre
Bibliographic record
Abstract
In Greenland, and across the Arctic, subsistence hunting has always been a critical part of the survival of Indigenous groups and their complex moral economies. For many Inuit and Inughuit communities in Greenland, this critical relationship with hunted animals, like the seabird the thick-billed murre (Uria lomvia or “Appa/Appat” in Greenlandic), has remained an important aspect of life even after Danish colonization, Home Rule, and Self-Government have changed the political and economic landscape in the country over the last 200 years. Within the last three decades, however, the thick-billed murre population in Greenland has been facing a decline with some colonies being lost in Southwestern Greenland, which has become the focus of scientific research conducted by seabird biologists in Greenland. This research, and the recommendations made by scientists based upon it has led to stricter hunting regulations and quotas affecting the relationship between hunters and their environment. In this case study of the thick-billed murre and its relationship with humans in Greenland, I utilize a political ecology analytical framework to examine through texts how authority is constructed by scientists to influence hunting regulation policy decisions. Through both thematic analysis and critical discourse analysis, I examine scientific studies, international and national reports, and existing regulations and recommendations. I demonstrate how the thick-billed murre population decline in Greenland is framed as an international and regional problem, but the primary solution that is recommended as the most “actionable” is further regulated local hunting in Greenland. I argue that by using their authority as scientific knowledge producers, scientists and conservationists control the narrative surrounding the population decline in Greenland and characterize hunting as a dysfunctional Inuit-species relationship that requires stricter regulations. However, as they utilize their authority to assert this narrative and to create and push their recommendations to Greenlandic lawmakers and international organizations, they fail to fully interrogate other factors behind the decline and rely on an outdated framework that places scientific knowledge over other forms of knowledge at the expense of Indigenous communities in Greenland.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".