Seeing Green: Lifecycles of an Arctic Agricultural Frontier<sup>☆</sup>
Bibliographic record
Abstract
Abstract Imaginaries of empty, verdant lands have long motivated agricultural frontier expansion. Today, climate change, food insecurity, and economic promise are invigorating new agricultural frontiers across the circumpolar north. In this article, I draw on extensive archival and ethnographic evidence to analyze mid‐twentieth‐century and recent twenty‐first‐century narratives of agricultural development in the Northwest Territories, Canada. I argue that the early frontier imaginary is relatively intact in its present lifecycle. It is not simply climactic forces that are driving an emergent northern agricultural frontier, but rather the more diffuse and structural forces of capitalism, governmental power, settler colonialism, and resistance to those forces. I also show how social, political, and infrastructural limits continue to impede agricultural development in the Northwest Territories and discuss how smallholder farmers and Indigenous communities differently situate agricultural production within their local food systems. This paper contributes to critical debates in frontiers and northern agriculture literature by foregrounding the contested space between the state‐driven and dominant public narratives underpinning frontier imaginaries, and the social, cultural, and material realities that constrain them on a Northwest Territories agricultural frontier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".