Growing Food at and through the Local Library: An Exploratory Study of an Emerging Role
Bibliographic record
Abstract
Abstract What does the intersection of food gardening and public librarianship look like? This chapter examines the question through a close analysis of three case studies that represent the spread of this phenomenon in the United States and Canada. This is a first step toward identifying areas for further research that will contribute to a more comprehensive understanding of how food gardening in and around public libraries addresses community-level health disparities. Although it is the case that food gardens and related programming are no strangers to public libraries, this topic has not received sustained attention in the LIS research literature. Public libraries have long been framed as key institutions in increasing consumer health literacy, but a more recent trend has seen them also framed as key institutions in promoting public and community health, particularly through the use of the public library space. This chapter examines food gardens at public libraries with this more expansive understanding of how public libraries address health disparities, by considering how this work occurs through novel partnerships and programs focused on transforming physical space in local communities. At the same time, public interest in food gardens parallels increased awareness of food in society; food and diet as key aspects of health; food justice activism; and a long history of community empowerment in the face of the proliferation of food deserts through myriad activities, including community food gardens. The authors consider how food gardening in public libraries parallels these trends.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".