Assessment of Agri-environmental Extension Services in Canada for Agriculture and Agrifood Canada
Bibliographic record
Abstract
Agri-environmental extension services are an important part of Canada’s agriculture system, but the extension landscape is changing with a variety of approaches across Canada. This study, commissioned by Agriculture and Agri-Food Canada and prepared by Groupe AGÉCO, provides an overview and an assessment of Canada’s agri-environmental extension services. The study’s objectives were to present an overview of key agri-environmental extension service providers for five regions: B.C, Prairies, Ontario, Quebec and Atlantic Canada; identify the strengths and gaps in the agri-environmental extension offering in each region under study; and propose key recommendations at the national level for improving the Canadian agri-environmental extension services ecosystem. The methodology was built on a desktop and literature review, interviews with representatives of extension delivery organizations (41 informants from 37 organizations across Canada) and a qualitative assessment of each region under study. Five factors were identified as essential for effective agri-environmental extension: long-term strategy and funding, professional training, availability, independence, and links with academia and research organizations. For each region under study, the project assessed agri-environmental extension based on each of these five dimensions. Key findings are presented below: Quebec stands out for the provision of agri-environmental extension in terms of funding, availability, professional training, and independence. Ontario and Saskatchewan distinguish themselves with respect to the strong connections between their provincial governments, academia and research organizations In B.C., more can be done to support producers’ extension needs, particularly in terms of funding and available expertise and capacity. The core recommendation from this review is to secure long-term funding for agri-environmental extension.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".