Farmers’ share of the consumer food dollar in Canada: What input‐output data from 1997–2021 show us
Bibliographic record
Abstract
Abstract This article uses Canadian input–output data from 1997–2021 to explore the consumer food dollar in terms of its distribution between farmers (i.e., farm share) and post‐farm gate industries. We have adopted the method developed by Canning (2011), which is based on a type one Input‐Output multiplier model. The overall farm share (19.4% in 1997–18.6% in 2021); the food at home farm share (23.7% in 1997–22.8% in 2021) and the food away from home farm share (10.1% in 1997–9.6% in 2021) did not fluctuate widely, suggesting a fairly fixed distribution between farmers and post‐farm gate industries. The overall farm share changed the most between 2019 and 2020 due to changing consumer behavior during the pandemic (COVID‐19). The time series econometric analysis on farm shares and price indices for the agri‐food value chain shows mild to significant associations among them. Given that, on average, 83% of every dollar Canadians spend on food goes to post‐farm gate sectors, it may be insightful to study post‐farm gate industries in greater detail in order to better understand the drivers behind recent food price inflation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".