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Record W2560854287 · doi:10.5304/jafscd.2016.071.007

Bridging Gaps: A Framework for Developing Regional Food Systems

2016· article· en· W2560854287 on OpenAlexafffundabout
Daryl Nelligan, Nairne Cameron, Brandon Mackinnon, Carter Vance

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsAlgoma University
FundersAlgoma University
KeywordsFood systemsBusinessSupply chainMarketingValue chainBridging (networking)DowntownIndustrial organizationAgricultureFood securityGeography

Abstract

fetched live from OpenAlex

Local food research has been generally focused on strengthening the alternative food system by scaling up local agriculture, rather than advancing strategies to bridge gaps between local farmers and conventional food retail businesses. Competitive advantage theory forms the foundation of a frame­work based on Porter’s (1985) firm (business unit) value chain for investigating food system gaps, and a logic model for promoting development by adding value throughout the alternative food supply chain. In the present study, a survey created jointly by local stakeholders investigated factors that food retail businesses consider when sourcing local food. Among the top rated factors, support­ing the local economy (opportunity) and regular delivery (barrier) were seen as significant to the regional food system of the Algoma District in central Canada. Mapping these factors through the firm value chain framework revealed a high degree of interconnectedness to other factors in the survey, including importance of obtaining fresh food, consistency of supply throughout the year, and reducing overall costs of supplying affordable products. Analysis of the survey results from the perspective of a food retail business pointed to information technology and coordinated distribu­tion methods as playing important roles in adding value to the regional food system. In addition to these results, the downtown of the study site has emerged as an aggregation point for local food, and local food may be playing a role in revitalizing the downtown. The value chain framework analysis can be applied to other localities to bridge gaps between local farmers and conventional supply chain 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.227
Teacher spread0.187 · 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
Published2016
Admission routes3
Has abstractyes

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