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Record W4224209899 · doi:10.3390/agriculture12050578

Opportunities and Challenges for Lebanese Horticultural Producers Linked to Corporate Buyers

2022· article· en· W4224209899 on OpenAlexaff
Walid Mukahhal, Gumataw Kifle Abebe, Rachel A. Bahn

Bibliographic record

VenueAgriculture · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBusinessQuality (philosophy)ProcurementSupply chainCertificationPosition (finance)MarketingIndustrial organizationCommerceFinanceEconomics

Abstract

fetched live from OpenAlex

This paper aims to analyze procurement decisions and contractual arrangements in the horticultural supply chain and evaluate opportunities for and challenges of horticultural producers linked to supermarkets and corporate restaurants in Lebanon. Accordingly, in-depth, semi-structured interviews were conducted with key horticultural supply chain actors in Lebanon. The study finds that corporate restaurants offer more opportunities for large horticultural producers and suppliers than supermarkets. Yet, corporate restaurants have more stringent quality requirements, as demonstrated by food safety certifications, and their contractual relationships are binding, symbiotic, and formal. Supermarkets source most of their products from wholesale markets and have opportunistic, non-binding relationships with their suppliers. In sum, the nature of the business relationships between horticultural producers and suppliers and corporate buyers depends on the ability of the producers to meet the quality requirements of the latter. Although corporate buyers have shown some interest in the local produce, they are yet to invest in local supplier development initiatives to enhance the capabilities of producers. Instead, corporate buyers resort to imports when the local producers fail to meet the quality standards or required volumes. The study suggests several alternative routes to enhance the market position of horticultural producers and suppliers in Lebanon.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.109
GPT teacher head0.230
Teacher spread0.121 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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