Securing Futures: The Inuvialuit Regional Corporation and Reindeer Herding History
Bibliographic record
Abstract
This article explores the complex history of reindeer herding in North America and contextualizes its connection to other Arctic Indigenous nations, from the S.mi people to its contemporary management by the Inuvialuit Regional Corporation (IRC). Reindeer herding was initially introduced in North America in the late nineteenth century as a solution to declining caribou populations and this practice evolved over time into its modern context with the IRC. By acquiring Canada’s only reindeer herd in 2021 and spearheading initiatives like the Country Food Processing Plant in Inuvik, the IRC is integrating traditional herding practices within a corporate framework to ensure sustainable development, food security, and local job creation, while also highlighting the importance of economic development in Indigenous self-determination. This article provides insight into how the IRC’s management of the reindeer herd represents an innovative model of Indigenous economic empowerment, blending culture with strategic economic initiatives to address contemporary challenges. The article contributes to the broader discourse on Indigenous governance, economic sustainability, and the pivotal role of traditional knowledge in shaping future pathways for Indigenous communities in the Arctic and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".