Bibliographic record
Abstract
First paragraphs: This fall 2023 issue of JAFSCD (volume 13, issue 1) includes open-call papers on a wide range of topics spanning the three main domains of a food system: production, marketing, and consumption. It also includes additional articles in response to our special call for papers on “Fostering Socially and Ecologically Resilient Food and Farm Systems Through Research Networks,” sponsored by INFAS, eOrganic, and USDA National Institute for Food and Agriculture. On our cover we see Michael Gavin, owner and operator of Root and Regenerate Urban Farms, using a seeder to plant a spring crop in one of the SPIN (Small plot IN-tensive) back yard plots in Calgary, Alberta, Canada. He collaborated with co-author Chelsea Rozanski, who is Ph.D. candidate in anthropology at the University of Calgary, on the article in this issue mentioned below. We begin the issue with John Ikerd’s Economic Pamphleteer column. In this first in a new series of columns he has titled “Perspectives on Agriculture, Food Systems, and Communities,” Ikerd calls for reforms requiring “changes in culture that prioritize resourcefulness, resilience, and regeneration over extraction, exploitation, and extermination.” I have more to say about John’s new series at the end of this editorial. . . .
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.316 | 0.162 |
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".