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Record W4405505567 · doi:10.1080/17448689.2024.2437578

Advancing food systems governance: Perspectives of Canadian civil society organizations

2024· article· en· W4405505567 on OpenAlexafffundabout
Charles Z. Levkoe, Johanna Wilkes, Peter Andrée

Bibliographic record

VenueJournal of Civil Society · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsWilfrid Laurier UniversityCarleton UniversityBalsillie School of International AffairsLakehead University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCivil societyCorporate governancePolitical sciencePublic administrationFood systemsEnvironmental ethicsFood securitySociologyPolitical economyLawManagementPoliticsEconomicsEcologyBiologyAgriculture

Abstract

fetched live from OpenAlex

Civil society organizations (CSO) engaged in food systems work have grown substantially in number, scale, and scope. Many of these CSOs are seizing opportunities to engage with the globalized industrial food system in efforts to promote greater equity and sustainability and have realized that addressing issues at a systemic level demand engaging with governance. Despite increasing scholarly attention to food systems and to CSO engagement with governments, the diversity of governance arrangements remains understudied. In this paper, we explore the meanings and perspectives of food systems governance from the standpoint of CSOs leaders across Canada and Indigenous territories. Drawing on 70 semi-structured interviews, we argue that CSOs play a central role in advancing food systems governance by how they frame and act on food issues. We point to CSOs understandings and engagement with food systems governance as broader and more nuanced than what has previously been documented in the literature. A more discerning and comprehensive understanding of how CSO actors describe and advance food systems governance helps articulate how CSOs are scaling-up place-based work, modelling new forms of governance, and ultimately, impacting decision-making structures.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.186
Teacher spread0.181 · 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
Published2024
Admission routes3
Has abstractyes

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