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Record W2782308604 · doi:10.20381/ruor-21348

Regional-Scale Food Security Governance in Inuit Settlement Areas: Opportunities and Challenges in Northern Canada

2018· dissertation· en· W2782308604 on OpenAlexaboutno aff
N Girard

Bibliographic record

VenueuO Research (University of Ottawa) · 2018
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)Food securityCorporate governanceGeographyScale (ratio)Food insecurityEnvironmental planningRegional sciencePolitical scienceEnvironmental resource managementBusinessArchaeologyCartographyEnvironmental scienceAgricultureFinance

Abstract

fetched live from OpenAlex

Food insecurity among northern Inuit communities represents a significant public health challenge that requires immediate and integrated responses. In the Inuvialuit Settlement Region (ISR), in the Northwest Territories (NWT), almost half of households experience some degree of food insecurity (33% moderate, 13% severe), and rates are even higher in Nunavut (35% moderate, 34% severe). Currently, food security issues in the Arctic are being addressed by multiple initiatives at different scales; however, the role that governance and policy plays in fostering or hampering Inuit food security remains under-evaluated. We took a participatory-qualitative approach to investigate how food security governance structures and processes are functioning in Inuit settlement areas, using case studies of the Inuvialuit Settlement Region (ISR) and Nunavut, the latter of which has already developed a food security strategy through significant community consultation. Using 18 semi-structured interviews, we examined the development and implementation of the Nunavut Food Security Strategy (NFSS) and Action Plan to identify challenges and lessons learned, identified governance challenges and opportunities in the current way food policy decisions are made in the ISR, and determined ways to improve governance arrangements to address Inuit food security more effectively at a regional scale. Participants implicated in the NFSS process identified a number of challenges, including high rates of employee turnover, coordinating work with member organizations, and lack of a proper evaluation framework to measure the Strategy’s outcomes. In terms of lessons learned, participants expressed the need to establish clear lines of accountability to achieve desired outcomes, and the importance of sufficient and sustained financial resources and organizational capacity to address food security in a meaningful way. Similar themes were identified in the ISR; however, top-down government decision-making at the territorial level and an absence of meaningful community engagement from program administrators during the conceptualization of food security interventions were specific issues identified in this context. In terms of opportunities for regional-scale food security governance, the Government of Northwest Territories (GNWT) is in the process of developing a Country Food Strategy that will engage with a range of stakeholders to develop a broader selection of country food programing. These findings suggest that food security governance remains a key challenge for Inuit. First, sufficient resources are needed to address food security in a sustained manner. Second, existing and planned food security policies and programs should include an evaluation component to demonstrate greater accountability towards desired outcomes. Finally, findings point to the need to develop new collaborative, integrated, and inclusive food security governance arrangements that take into account local context, needs, and priorities. The NFSS is a useful model for collaborative food security governance from which other Inuit regions can learn and adapt.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0210.005
Scholarly communication0.0060.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.147
GPT teacher head0.354
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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