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Record W4240687078 · doi:10.24124/2015/bpgub1109

Consuming connections: experiences of food systems during times of homelessness in Prince George, British Columbia

2015· dissertation· en· W4240687078 on OpenAlexaboutno aff
Julia Russell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFood sovereigntyFood securityFood systemsFood studiesVariety (cybernetics)Focus groupGeorge (robot)Food insecurityPromotion (chess)GeographyPolitical scienceSociologyMarketingBusinessEcologyHistoryAgriculturePolitics

Abstract

fetched live from OpenAlex

This research sought to explore the seasonal dimensions of food security for people experiencing homelessness in Prince George, BC and the effects of this on their health and well-being. Data were collected using a modified approach to community mapping, a focus group and semi-structured interviews. The results indicate that people have a wide variety of strategies that they employ to access food. There was a strong desire for more culturally appropriate food to be provided through charitable food aid and for participants to become more actively engaged in producing their own food. Physical environments, social environments and relationships were found to influence what food people consumed, and there were important seasonal trends in food availability and accessibility. A holistic approach that can accommodate complexity is necessary to improve food security and health, thus the promotion of Indigenous food systems and Indigenous food sovereignty are seen as important future directions. --Leaf ii.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.553

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.006
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.404
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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