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Record W3012317091 · doi:10.1017/s0260210520000017

Democratising food: The case for a deliberative approach

2020· article· en· W3012317091 on OpenAlexfundno aff
Merisa S. Thompson, Alasdair Cochrane, Justa Hopma

Bibliographic record

VenueReview of International Studies · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersUniversity of WaterlooUniversity of Sheffield
KeywordsDeliberationPolitical scienceFood sovereigntyFood securityDeliberative democracyScope (computer science)Food systemsSustainabilityStatus quoEconomic JusticeDemocracyPoliticsEnvironmental ethicsLaw and economicsSociologyLaw

Abstract

fetched live from OpenAlex

Abstract Prevailing political and ethical approaches that have been used to both critique and propose alternatives to the existing food system are lacking. Although food security, food sovereignty, food justice, and food democracy all offer something important to our reflection on the global food system, none is adequate as an alternative to the status quo. This article analyses each in order to identify the prerequisites for such an alternative approach to food governance. These include a focus on goods like nutrition and health, equitable distribution, supporting livelihoods, environmental sustainability, and social justice. However, other goods, like the interests of non-human animals, are not presently represented. Moreover, incorporating all of these goods is incredibly demanding, and some are in tension. This raises the question of how each can be appropriately accommodated and balanced. The article proposes that this ought to be done through deliberative democratic processes that incorporate the interests of all relevant parties at the local, national, regional, and global levels. In other words, the article calls for a deliberative approach to the democratisation of food. It also proposes that one promising potential for incorporating the interests of all affected parties and addressing power imbalances lies in organising the scope and remit of deliberation around food type.

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)0.063
metaresearch head score (Gemma)0.037
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: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.076
Scholarly communication0.0170.016
Open science0.0030.011
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0030.001

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 head0.091
GPT teacher head0.302
Teacher spread0.210 · 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
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

Citations36
Published2020
Admission routes1
Has abstractyes

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