Public Engagement and the Nunavut Roundtable for Poverty Reduction: Attempting to Understand Nunavut’s Poverty Reduction Strategy
Bibliographic record
Abstract
The 2009 Government of Nunavut Report Card, a review of the first ten years of Nunavut’s existence, recommended the development of an anti-poverty strategy to help address severe social inequality in the territory. Between October 2010 and November 2011, the Government of Nunavut (GN), jointly with Nunavut Tunngavik Incorporated (NTI), oversaw an extensive poverty-reduction public engagement process that resulted in the creation of the Nunavut Roundtable for Poverty Reduction and the territory’s poverty reduction strategy. The strategy suggests that the tension that exists between Inuit forms of governance and the model of public governance used today is the root cause of poverty. However, it does not offer an official definition of the term. Knowing the way in which poverty is perceived in Nunavut is key to understanding the direction of the territory’s poverty reduction strategy. Drawing upon interviews conducted in Iqaluit and in Ottawa in 2015, as well as on records from the Nunavut Anti-Poverty Secretariat, this article examines how the territory’s poverty reduction strategy was developed. It argues that the roundtable’s participatory methods, closely aligned with principles of the Nunavut Land Claims Agreement, have fostered a politicized discussion about poverty that has resulted in Nunavut’s poverty reduction strategy’s focus on collaboration and healing.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".