The value of Canadian agriculture: Direct, indirect, and induced economic impacts
Bibliographic record
Abstract
While the value of agriculture to the Canadian economy is well established, its extensive indirect and induced value through upstream and downstream industries is not. Input-Output (I/O) analyzes are a common tool that measure the direct, indirect and induced impacts of an industry to the entire economy. We reviewed I/O analyzes that used economic multipliers to estimate the total contribution of agricultural industries to Canada's economy. Reports underwent data extraction for output, Gross Domestic Product (GDP), jobs, labor income and taxes generated. We found that when indirect and induced economic impacts are considered, the value of agricultural industries is much greater than traditional valuations indicate. Beef and canola were the two largest industries in terms of GDP and jobs, with direct impacts constituting less than half of their total impacts. Recent and thorough I/O analyzes are available for only a limited number of agricultural industries. There is a need for I/O analyzes covering key agricultural industries at the regional and national level using uniform methodology and recent data and multipliers. This information is essential to gain a systemic understanding of the true economic value of agriculture and to inform policies and investment that maximizes the potential of agricultural industries.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".