Piecing together the Inuit food security policy puzzle in Nunatsiavut, Labrador (Canada): protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Inuit Nunangat is the Inuit homeland in Canada. It is comprised of four Inuit regions. Inuit residing in these areas experience greater social and economic inequities than the general Canadian population. Food security exemplifies this inequity and is a distinct determinant of Inuit health. Policy can play an integral role in health equity. However, demonstrating this role can be a complex task, especially when there are both national and regionally specific policies pertaining to each of the Inuit regions. This scoping review will focus on Nunatsiavut, located in northern Labrador. This region is situated within a complex policy space due to the national, provincial and regional governance structures, geographical location and the breadth of factors pertaining to food security. This scoping review aims to identify the range of policies pertaining to food security in Nunatsiavut and complete a directed content analysis to code each policy against the applicable dimension of food security. METHODS AND ANALYSIS: The researchers will conduct a search strategy on the following four databases: MEDLINE (via Ovid), Embase (via Ovid), CINHAL and Scopus. A hand search of the relevant journals, conference abstracts and grey literature will be completed from April to October 2019. The following parameters will be extracted: a description of the policy, the organisation/institution that developed the policy, the definition of food security used or implied, and any stated intended targets or outcomes. The results will be compiled in a tabular form. ETHICS AND DISSEMINATION: Ethics approval is not required as primary data will not be collected. The findings from this scoping review will be disseminated through peer-reviewed journals and public presentations. The results of this scoping review will be validated by a Nunatsiavut Government Advisory Group.
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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 | 0.089 | 0.095 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".