An investigation of the Leontief Paradox using Canadian agriculture and food trade: an input-output approach
Bibliographic record
Abstract
This study investigated whether the Leontief Paradox existed for Canadian agriculture and food trade in 2006. Factor intensities in exports and import replacements of agriculture and processed food commodities were estimated using both the Leontief and Leamer approaches. The Leamer approach provided additional information on factor endowment abundance. Statistics Canada's 2006 Input-Output tables were modified to provide an input-output model that was disaggregated in both agriculture and processed food sectors and in agriculture and food commodities. The modified version of the Input-Output model was used to estimate the factor intensity and factor abundance in Canadian agriculture and food trade. Production factors included in this study were capital, labour, and land. The results from both the Leontief and Leamer approaches suggested that Canadian agriculture and food exports were relatively capital and land intensive, while its import replacements were relatively labour intensive in 2006. This finding does not support the existence of the Leontief paradox for Canadian agriculture and process food trade. In addition, the Leamer approach suggests that Canada has an abundance of capital and land in comparison to labour.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".