Bibliographic record
Abstract
Northern Manitoban (Canada) communities experience much higher rates of food insecurity than the rest of Manitoba. While the Manitoba food insecurity rate is 12.1 percent, the incidence in northern Manitoba is 75 percent. This is due to geographic, transportation, economic, and knowledge barriers that particularly affect remote northern communities. The severity of food insecurity can vary from worrying about running out of food to going days without eating. Food insecurity is a threat to physical and mental health, and can lead to increased risk of illness, such as depression, diabetes, and heart disease. Following a presentation of food insecurity in Manitoba at Expo 2015, this paper looks at the community of Cross Lake as a case study of methods to address food insecurity. While the community is faced with many barriers to food security, it also has many food assets. Food Matters Manitoba, a non-governmental organization with the mission to end food insecurity in Manitoba, has engaged with Cross Lake through a process of community engagement in order to find out what community members think are most important projects to work on and what resources already exist. From this process it is clear that Cross Lake has many assets, but there is still a lot of work that needs to be done in order to significantly improve its food security. Community-driven economic development that is participatory, comprehensive, and takes into account traditional practices can provide the means to address food insecurity and reduce poverty and social exclusion. A community-based approach also provides opportunities for northern communities to make connections with each other and create geographic partnerships that empower local residents to improve their own conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".