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Record W4389986681 · doi:10.5304/jafscd.2023.131.023

In This Issue: Organic research networks and more!

2023· article· en· W4389986681 on OpenAlexaboutno aff
Duncan Hilchey

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Food systemsYardAgricultureCover cropSociologyPolitical scienceEnvironmental ethicsLibrary scienceHistoryFood securityAgroforestryEnvironmental scienceComputer scienceArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.262
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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