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Record W3047992578 · doi:10.1017/sus.2020.19

Assessment of the growth in social groups for sustainable agriculture and land management

2020· article· en· W3047992578 on OpenAlexaff
Jules Pretty, Simon Attwood, Richard Bawden, Henk van den Berg, Zareen Pervez Bharucha, John Dixon, Cornelia Butler Flora, K Gallagher, Ken Genskow, Jan Willem Ketelaar, Japhet K. Kiara, Vijay Kumar, Yuelai Lu, Tom MacMillan, Anne Maréchal, Alma Linda Morales-Abubakar, Andrew Noble, P. V. Vara Prasad, Ewald Rametsteiner, John P. Reganold, Jacob I. Ricks, Johan Rockström, Osamu Saitô, Peter S. Thorne, Songliang Wang, Hannah Wittman, Michael Winter, Puyun Yang

Bibliographic record

VenueGlobal Sustainability · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRestructuringSocial capitalAgricultureGrowth managementSustainabilityLand managementEquity (law)Economic growthLand useBusinessSocial sustainabilitySocial equalityProductivityAgricultural economicsNatural resource economicsDevelopment economicsEconomicsGeographyPolitical scienceMarket economyFinance

Abstract

fetched live from OpenAlex

Non-technical summary Until the past half-century, all agriculture and land management was framed by local institutions strong in social capital. But neoliberal forms of development came to undermine existing structures, thus reducing sustainability and equity. The past 20 years, though, have seen the deliberate establishment of more than 8 million new social groups across the world. This restructuring and growth of rural social capital within specific territories is leading to increased productivity of agricultural and land management systems, with particular benefits for those previously excluded. Further growth would occur with more national and regional policy support.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations93
Published2020
Admission routes1
Has abstractyes

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