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Queering food security research: A critical analysis of 2SLGBTQ+ People's experiences of food insecurity in Toronto during the COVID-19 pandemic

2024· article· en· W4392112457 on OpenAlexafffundabout
James K. Gibb, Sarah Williams, Kaspars Mikelsteins, Jada Charles, Leela McKinnon, Laura Beach, Luseadra McKerracher, Jessica Fields

Bibliographic record

VenueSocial Science & Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of British ColumbiaThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Toronto ScarboroughCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsFood securityCoronavirus disease 2019 (COVID-19)PandemicFood insecurity2019-20 coronavirus outbreakEconomic growthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SociologyPolitical scienceSocioeconomicsEnvironmental healthGeographyMedicineVirologyEconomicsAgricultureOutbreak

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.020
metaresearch head score (Gemma)0.039
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 score0.171
Threshold uncertainty score0.715

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.039
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0210.015
Scholarly communication0.0100.004
Open science0.0020.014
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.340
GPT teacher head0.581
Teacher spread0.241 · 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

Citations7
Published2024
Admission routes3
Has abstractno

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