IMPACT OF COVID-19 ON FOOD PRICES IN REMOTE INDIGENOUS COMMUNITIES- THE CASE OF FOX LAKE IN LITTLE RED RIVER CREE NATION (LRRCN).
Bibliographic record
Abstract
Recent studies suggest significant increase in food prices due to the Covid-19 pandemic in Indigenous communities across Canada and the US. This study investigates whether and to what extent food prices might have changed in response to the Covid-19 pandemic using Fox Lake, a remote community within Little Red River Cree Nation in Northern Alberta, as an interesting case study. The study uses retail price data for selected food items from Fox Lake’s local store, The Northern, and respective retail prices for similar products in Alberta, over a five-year period from 2017 to 2021. The study is guided by three specific objectives. First, it investigates disparities in price levels and temporal price changes between Fox Lake and Alberta. Second, the study assesses the extent to which temporal changes in food prices may have been driven by Covid-19 in Fox Lake in comparison to respective changes at provincial level. Finally, the study examines the implications of potentially higher food prices on the living wage requirements for Fox Lake households.\nResults show that between 2017 and 2022, Fox Lake's prices for selected food items were up to 175% and 186% higher than in Alberta, particularly for food items like canned tuna and margarine respectively. Food prices in Fox Lake, however, did not soar during the pandemic. On average prices increased by 1.1% in Fox Lake, which is three times lower than the average food price increase in Alberta. The thesis’ findings are, hence, in contrast to significant price surges of 400%, observed during the Covid-19 pandemic e.g., in northern Ontario. Nevertheless, a modest 1.1% increase in food prices would increase annual food expenses by $179 and require a living wage increase from $19.58 to $19.63 for a family of four with two working members.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".