Bibliographic record
Abstract
Scientists widely accept that modern agriculture is unsustainable, but the best methods for addressing unsustainability are still contested (Constance, Konefal, and Hatanaka 2018). Grassroots sustainable agriculture communities have long participated in the exploration of solutions for agriculture unsustainability, and their momentum continues to grow in the technical age. Practitioners of grassroots sustainable agriculture use many information systems that were not originally built to support the design of agricultural systems. Based on ethnographic research with two grassroots sustainable agriculture communities, I show that participants’ personal and community values frequently clashed with those embedded in information systems, including ones used to look for and manage plant information. Furthermore, I demonstrate a range of information challenges that participants faced in the absence of tools designed to support their specific work. I argue that practitioners of grassroots sustainable agriculture need information systems tailored to their goals and values in order to productively address barriers to designing and building agroecosystems for their communities.This dissertation provides an example of how to involve communities in the development of information technology artifacts and strengthen efforts to support sustainability via technological interventions. First, I engaged in two grassroots sustainable agriculture communities as a participant, experiencing their practices, values, and information challenges first hand. Then, I worked with the communities to create a plant database web application (SAGE Plant Database) that supports agroecosystem design in local contexts. Members of the communities participated in the design, development, and data population stages so that the SAGE Plant Database supports their design context and upholds their technological and holistic sustainability values. At the foundation of the database is a plant ontology grounded in the participants’ practice of designing agroecosystems. My comparative analysis of the design of the SAGE Plant Database to other databases demonstrates its relevance due to its emphasis on agroecological relationships among plants and between plants and the environment, the inclusion of ethnobotanical data, and the embedded community values. By engaging in this research, I seek to make progress towards transforming the technology-supported food system into one that furthers food security, food sovereignty, and holistic sustainability.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".