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Record W2788872910 · doi:10.15353/cfs-rcea.v5i1.230

Finding formula: Community-based organizational responses to infant formula needs due to household food insecurity

2018· article· en· W2788872910 on OpenAlexaffvenueabout
Lesley Frank

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsAcadia University
Fundersnot available
KeywordsFood insecurityInjusticeFood securitySample (material)Infant formulaWelfarePovertyBusinessEconomic growthPolitical scienceEconomicsPsychologyGeographySocial psychologyMedicine

Abstract

fetched live from OpenAlex

This paper reports on qualitative research concerning community-based organizational responses to infant formula needs due to household food insecurity. It explores this topic against the backdrop of neo-liberal social welfare approaches that shape gendered food work within food insecurity households, as well as current state approaches to infant feeding policy targeted to vulnerable populations. Based on telephone interviews with a random sample of organizations across Canada (N=26) in 2016, this paper details typical responses to infant food insecurity within a sample of family resource projects with funding from the Canada Prenatal Nutrition Program, as well as typical responses from a sample of food banks. Results demonstrate that neither state nor community organizations adequately respond to infant food insecurity. This leads to serious problems of unequal access, potential food risk, and food injustice that are imposed on mothers and formula-fed infants when mothers are forced into situations of pathologized foraging to find formula. This paper argues that infant food insecurity is the result of a succession of public policy failures that are best addressed with a reflexive, feminist, food justice approach.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.266
GPT teacher head0.392
Teacher spread0.126 · 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 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

Citations7
Published2018
Admission routes3
Has abstractyes

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