Reflections on technological progress in the agri‐food industry: Past, present, and future
Bibliographic record
Abstract
Abstract Technological advances—for example, from hand milking to robotic milking—are at the heart of economic transformation and have significantly shaped the agri‐food industry and economic growth throughout history. A look at the lead article of the first issue (and the first volume, 1952) of theCanadian Journal of Agricultural Economics(CJAE) 70 years ago reveals an ongoing inquiry within the discipline about how technological progress has shaped how we manage our farms with the implications on aggregate industry productivity and food prices. The topics discussed along these lines in the first issue of the CJAE are still relevant today—for example, challenges with measuring productivity and innovation, diffusion of innovation, technological unemployment, demand for skilled workers, financing innovations, climate change and food security. Science, technology, and innovation for the 21st century hold the potential to foster resilient and sustainable intensification of farm production and productivity growth for the agri‐food industry. In this address, I reflect on the past, present, and future impacts of technological innovations and productivity growth on the agri‐food industry and discuss the implications for future research, welfare, and policy.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".