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Record W4386895561 · doi:10.3390/foods12183492

Unraveling Elusive Boundaries: A Comprehensive Framework for Assessing Local Food Consumption Patterns in Nova Scotia, Canada

2023· article· en· W4386895561 on OpenAlexaffabout
Sylvain Charlebois, Marie Le Bouthillier, Janet Music, Janèle Vézeau

Bibliographic record

VenueFoods · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsAgriculture and Agri-Food CanadaUniversité LavalDalhousie University
Fundersnot available
KeywordsNova scotiaNova (rocket)Consumption (sociology)Benchmark (surveying)Food consumptionFood systemsGeographyRegional scienceAgricultural economicsFood securityEconomicsSociologyEngineeringCartographySocial scienceAgriculture

Abstract

fetched live from OpenAlex

Promoting local food consumption for economic growth is a priority; however, defining "local" remains challenging. In Nova Scotia, Canada, this pioneering research establishes a comprehensive framework for assessing local food consumption. Employing three data collection methods, our study reveals that, on average, Nova Scotians allocate 31.2% of their food expenditures to locally sourced products, excluding restaurant and take-out spending, as per the provincial guidelines. The participants estimated that, in the previous year, 37.6% of their spending was on local food; this figure was derived from the most effective method among the three. However, the figure was potentially influenced by participant perspective and was prone to overestimation. To enhance accuracy, we propose methodological enhancements. Despite the limitations, the 31.2% baseline offers a substantial foundation for understanding local food patterns in Nova Scotia. It serves as a replicable benchmark for future investigations and guides researchers with similar objectives, thereby establishing a robust research platform.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.908

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 studies0.0000.000
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 head0.040
GPT teacher head0.270
Teacher spread0.230 · 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 designObservational
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

Citations26
Published2023
Admission routes2
Has abstractyes

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