Assessing the Constraints to the Adoption of Containerized Agriculture in Northern Canada
Bibliographic record
Abstract
Considerable advancements are being made in containerized agricultural systems in the northern Canada. These systems are proving successful at overcoming the environmental constraints associated with cold climate food production and hold great promise for remote communities that suffer from high rates of food insecurity. However, if new technologies are to provide lasting and meaningful change for northern communities, critical attention needs to be directed to the variable and complex constraints that may limit their adoption and scalable success. To evaluate the potential uptake and use of containerized agriculture in northern Canada we employed the Adoption and Diffusion Outcome Prediction Tool. Twenty-two variables were ranked according to their influence on adoption. Six variables were then identified as being most constraining to the adoption of containerized agricultural systems, including upfront costs, expected profits, environmental impacts, complexity of the technology, trialability, and reversibility. We believe this type of pre-assessment is a critical, yet often over-looked step in technology transfer, and a necessary stage in assessing the scaling out potential for new food production technologies. This is particularly important for new food production technologies that demand significant financial investments that are wholly or partially irreversible.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".