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Record W4389918478 · doi:10.5206/iveypub.78.2023

Towards a Climate-Smart Food System: A Theory of Change and Impact Metrics to Trigger Farming and Societal Change

2023· report· en· W4389918478 on OpenAlexaboutno aff
Jean-Francois Obregon, Sérgio G. Lazzarini, Diane‐Laure Arjaliès, Julie Gualandris, Guanjie Huang, Ellen Kempton, Rubaina Singla, Yashika Sharma

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsFood systemsFood processingIncentiveSustainable agricultureBusinessAgricultureFood securityPsychological interventionAgricultural productivitySustainable developmentSustainabilityNatural resource economicsEnvironmental resource managementEnvironmental economicsEnvironmental planningEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

There is significant interest in sustainable food production practices in Canada and worldwide due to the challenges caused by the Russia-Ukraine war, land degradation, and climate change. Sustainable food production is a food system that provides affordable, nutritious food while preserving and restoring natural resources and generating robust ecosystem services such as carbon sequestration, water filtration, and retention. This report explores multiple routes to foster improved social, ecological, and economic impacts associated with alternative practices promoting sustainable food production. It identifies core problems that prevent agricultural systems and their food chains from implementing (more) sustainable practices. The report mobilizes a Theory of Change (TOC) to outline possible interventions and metrics to implement (community-based) interventions to promote shared principles of sustainable production and create communities of practice. The TOC was developed in consultation with a set of actors in the food chain (including farmers, financial institutions, municipal governments, food processors, NGOs and industry associations) during a nine-month research intervention in Canada (2023), complemented by a literature review. Thanks to this co-creation process, the proposed interventions and metrics to measure and track improvements at the farm and societal levels presented in this report are outcomes-based and bottom-up. This enables agricultural communities and actors in the food chain to pursue alternative routes to improve outcomes. The report also discusses incentives to pursue sustainable food production, either explicit (e.g. monetary payments, contractual clauses) or implicit (e.g. social norms, cultural values, network-based engagement of food chain actors). Lastly, it outlines a potential research design to test the suggested interventions, metrics, and incentives in a Randomized Control Trial (RCT).

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)0.009
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0020.020
Scholarly communication0.0090.011
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.141
GPT teacher head0.326
Teacher spread0.185 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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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