Country food consumption in Yukon, Northwest Territories and Nunavut, Foodbook study 2014–2015
Bibliographic record
Abstract
BACKGROUND: This article presents a descriptive summary of the consumption of various country food (i.e. locally harvested plant and animal foods) products by residents of Yukon (YT), Northwest Territories (NT) and Nunavut (NU). Data were collected as part of the Foodbook study in 2014-2015. METHODS: The Foodbook study was conducted by telephone over a one-year period. Respondents were asked about consumption of a wide range of food products over the previous seven days. Residents of the territories were also asked about consumption of regionally-specific country food. Data were weighted to develop territorial estimates of consumption. Data on age, gender, location, income and education were also collected. RESULTS: The national response rate for the Foodbook survey was 19.9%. In total, 1,235 residents of the territories participated in the study (YT, n=402; NT, n=458; NU, n=375). Consumption of any country food during the previous seven days was reported by 77.5%, 60.7%, and 66.4% of participants in NU, NT and YT, respectively. CONCLUSION: Responses to country food questions asked alongside the main Foodbook questionnaire provide insight on country food consumption in YT, NT and NU.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".