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Record W6889117421 · doi:10.25384/sage.c.5853130

A Food Security Indicator Framework for British Columbia, Canada

2022· other· en· W6889117421 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityPublic healthConceptual frameworkStakeholderFood systemsPopulation healthProcess (computing)Grey literature

Abstract

fetched live from OpenAlex

Food security is a determinant of health and increasingly recognized as a focus for health promotion. Led by the Population and Public Health Program, British Columbia Centre for Disease Control, this article outlines the process of development and the evidence-based conceptual framework that guides the systematic selection of food security indicators in the Province. A phased, iterative approach to develop the food security framework was adopted. Phase 1 consisted of a literature search of food security indicator models, and key informant discussions. Phase 2 consisted of modification of the model based on stakeholder consultation. The framework development occurred between January 2016 and April 2019. A structured scan of the literature found no existing conceptual frameworks specific to food security indicators in the Global North. The most relevant and frequently used frameworks for indicator reporting identified were environmental health indicator frameworks. This article presents a matrix framework based on existing environmental health indicator frameworks. It integrates environmental health causal networks (e.g., determinants–current state–impact–response) with food security elements identified as (a) individual and household food insecurity, (b) food systems, and (c) capacity. This framework contributes to food security performance monitoring in the Global North and fills an important gap in evaluating the impact of the public health response to food security. Use of this comprehensive framework can enable program planners and policy makers to be clear about where and how they are attempting to assess, influence and monitor food security, and illustrate the interconnectedness between indicators.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0050.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4390.000

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 head0.029
GPT teacher head0.286
Teacher spread0.257 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

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