Trials, Tribulations, and Transitions: A Case Study of the Huerta del Valle Community Garden
Bibliographic record
Abstract
We, Zavi Engles and Ru Apt, would like to thank all of those involved in this project and the conducting of this research for their boundless support, input, and enthusiasm. In particular, we would like to thank L.M., the garden manager, for all of her patience in working with us and her passion and dedication to doing whatever necessary to make this project (and the research that will hopefully facilitate this project) succeed. L.M.’s entire family has also been endlessly hospitable to us, providing a second home for us in Ontario as well as comfort and support throughout the entire semester. We would like to thank the committed Huerta del Valle and the children from the Mira Loma Community Center walking club for persevering in their dedication to seeing this community project through. We would also like to thank our professors for their guidance in our learning process, internship experience, and ongoing research throughout the semester. We would like to acknowledge the Pitzer in Ontario’s Urban Fellow for going above and beyond her listed duties to support us and Huerta del Valle throughout the semester. Additionally, we would like to thank F.P., A.H., and O.P. for their contributions and support throughout the semester. Finally to all of our fellow Pitzer friends and cohorts in this community gardening project--thank you for the work you have done, the ways in which you have helped us,
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".