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Record W4281554689 · doi:10.3389/fsufs.2022.870412

Historical Construction of Local Food System Transformations in Lebanon: Implications for the Local Food System

2022· article· en· W4281554689 on OpenAlexaff
Walid Mukahhal, Gumataw Kifle Abebe, Rachel A. Bahn, Giuliano Martiniello

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFood systemsGeopoliticsAgriculturePsychological resiliencePoliticsContext (archaeology)Food securityPolitical scienceResilience (materials science)Economic systemEconomic geographyPolitical economyEconomyDevelopment economicsGeographyEconomics

Abstract

fetched live from OpenAlex

Food system transformations occur in a complex political, economic, social, and territorial landscape. The study provides a historical construction of global food regime changes and the adaptiveness, transformability, and resilience of the local food system in Lebanon, a Middle Eastern context. Lebanon offers a unique opportunity to understand the influence of global food regimes and geopolitics on agriculture, the local food system, and capital accumulation. After the 1975–1990 Lebanese Civil War, Lebanon experienced food retail transformation and international penetration through foreign investments. These alterations have several implications for society and the local food system: farming households' influence on agricultural policies and the political commitment to support the farming community decreased. The paper concludes that Lebanon's local food system transformation is a manifestation of geopolitical events and global food regime changes. This may have important implications and pave the way for a new food system that is based on the revitalization of agriculture and new forms of geoeconomic partnerships with regional actors.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.185
Teacher spread0.173 · 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 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

Citations9
Published2022
Admission routes1
Has abstractyes

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