Assessing the Gap Between Intention and Implementation in Seed Lending Libraries
Bibliographic record
Abstract
A Review of: Cohn, S. B. (2024). Lending seeds, growing justice: Seed lending in public and academic libraries. The Library Quarterly, 94(2), 117–133. https://doi.org/10.1086/729231 Objective – To survey seed lending programs in libraries on their goals and marketing. The research questions focus on the theoretical underpinnings of creating a seed library, the promotion and marketing materials used, and the gaps and connective points between the marketing and the underpinned intent, especially surrounding activism. Design – Literature review and survey. Setting – Academic and public libraries in the Seed Lending Network in the United States and Canada. Subjects – Librarians responsible for seed lending programs identified on each library’s website. Methods – Online survey with a mix of open- and closed-ended questions covering reasons for starting seed lending and workshops and other programming around seeds. Main results – 58 completed surveys were returned, 42 from public libraries and 16 from academic libraries. Some academic libraries who curate seed libraries do so in support of their agricultural degree programs, while others noted campus community gardens. Public libraries responded that their primary purpose was to support community gardeners; some less neutral responses included food deserts and rural libraries concerned about the loss of heirloom and non-GMO seeds. The marketing and programming around these programs primarily focused on gardening and how-to classes, including making paper with food scraps, creating natural dyes, and others. There is a gap between the initial intentions that were less neutral and the programming language that is more neutral and less activist. Conclusion – Some surveyed seed libraries stated goals around food justice, but there is a gap between that intention and the more neutral marketing and programming for their seed libraries. This could be due to preservation in a precarious time for public libraries.
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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 | 0.064 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".