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Record W3202691045 · doi:10.14430/arctic73390

Valuation of Country Food in Nunavut Based on Energy and Protein Replacement

2021· article· en· W3202691045 on OpenAlexaffvenueabout
Duncan William Warltier, Manuelle Landry‐Cuerrier, Murray M. Humphries

Bibliographic record

VenueARCTIC · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsValuation (finance)Food systemsSubsidyBusinessValue (mathematics)IndigenousAgricultureAgricultural economicsGeographyEconomicsFood securityBiologyEcologyMarket economy

Abstract

fetched live from OpenAlex

Communicating value across the pluralities of Indigenous Peoples’ food systems requires attention to economy and environment, food and wildlife, and the health of the people and that of the land. Valuation of distinct entities is always difficult but often essential to describe collective wealth and well-being, to quantify trade-offs, and to consider compensation when one is compromised for another. Here we estimate the replacement value of Nunavut country food by combining information on the amount and nutritional composition of harvested country food with the nutritional content and local price of store-bought food. Comparing the five-year average of energy and protein available in reported harvest to recommended dietary allowances indicates that 17 of 21 Nunavut communities harvest enough country food to satisfy the protein requirements of all community members. Nunavut’s country food system annually harvests five million kg of protein-rich food from across the territory, which would cost $198 million to purchase as store-bought protein, with a replacement value between $13.19 and $39.67 per kg depending on energy versus protein replacement and the inclusion versus exclusion of store-bought food subsidies. These valuations are higher than most previous estimates of local food value because they are more reflective of the energy and nutrient richness of country food and the high price of store-bought food in northern communities. The country food system is priceless in many, profound ways; better awareness of its energy and protein cost of replacement, together with the breadth of its nutritional and cultural value, may help to ensure local food systems are prioritized in northern food security and economic development initiatives.

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 score0.107
Threshold uncertainty score0.907

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 head0.042
GPT teacher head0.336
Teacher spread0.294 · 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

Citations9
Published2021
Admission routes3
Has abstractyes

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