Finding a Middle Way to Sustainable Food Systems
Bibliographic record
Abstract
First paragraphs: The premise of Susan Futrell’s Good Apples: Behind Every Bite is that by understanding the environmental, social, and economic issues affecting apples growers in America, the reader can better appreciate and support sustainable food systems. Futrell’s storytelling is grounded in her years of experience working in sustainable food distribution, which includes 25 years in sales and marketing for a cooperatively owned natural food distributor called Blooming Prairie Warehouse in the Midwest, and her current work with Red Tomato, a small nonprofit food hub based in Massachusetts, where she helped develop the Eco Apple® program. From the beginning, Futrell resists the pressure to simplify and dichotomize complexities. Chapter 1, At the Intersection of Apples and Local, establishes this tone with her contextual consideration of how the term local is defined. Chapter 2, Immigrant Apples, reviews the history of apples in America. In it she discusses key historical figures and the emergence of seedling nurseries, apple varieties, growers’ associations, and land-grant institutions. . . .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".