MétaCan
Menu
Back to cohort
Record W6980538301

Charitable and Community Food Access in Greater Victoria: Understanding the Lived Experience of Mothers and Caregivers

2022· other· en· W6980538301 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2022
Typeother
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsFood insecurityLived experienceMeaning (existential)Mental healthQuality (philosophy)Food security
DOInot available

Abstract

fetched live from OpenAlex

Food insecurity affects 9.6% of Canadians, meaning that individuals and families are unable to access or consume a sufficient or adequate diet quality in socially acceptable ways. Previous research has shown that in Canada, mothers and caregivers are more likely to experience food insecurity, which has negative effects on mental and physical health outcomes, social positionality, and wellbeing for them and their families. As a response to increasing food insecurity in the global North, food access services have been emerging since the 1980s in attempts to remediate the experience of food insecurity; however, there has been debate surrounding the efficacy of food access services. This research analyzes the experience of mothers and caregivers with dependents in Victoria BC who use food access services, including food banks, community food models, or food hamper services. This research argues that food access services can be improved by adopting a community-focused right to food approach in Greater Victoria to further assist mothers and caregivers in need. From the interview data of five participants, results showed that three main themes emerged with regards to experience with food insecurity in Victoria, and 15 suggestions were provided for food access organizations based on results.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.292
Threshold uncertainty score0.587

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.265
Teacher spread0.219 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueUVic’s Research and Learning Repository (University of Victoria)Same topicInfrastructure Maintenance and MonitoringFrench-language works237,207