Seed saving in Atlantic Canada: Sustainable food through sharing and education
Bibliographic record
Abstract
Seed saving is an important element of seed security. Seed saving can support biodiversity, nourish food systems, facilitate environmental education, and enable the creation of networks that support food sovereignty. Public interest in seed security is on the rise, but local resources and funding to support seed activities is limited. The survival of seed collections, libraries, banks, and farms depends on personal relationships within the seed community. While Atlantic Canada’s seed saving community is scattered geographically, it is tightly knit. Seed savers share knowledge, information, and tools, sometimes between competitor businesses. At times, information is shared between those with commercial interests, such as seed companies, and public events such as seed swaps, as individual success is contingent on the overall health of the seed system. In this field report, we synthesize findings from three case studies on seed saving in Atlantic Canada, which map regional seed activities, and detail the opportunities and challenges that such initiatives face. While Atlantic Canada has seen growth in the number and scale of both public and private seed saving initiatives, much work remains to be done. Nevertheless, the initiatives constitute a critical mass that can benefit from this assessment upon which future actions can be based.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".