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Record W2319438057 · doi:10.15353/cfs-rcea.v3i1.72

Challenges to acquiring and utilizing food literacy: Perceptions of young Canadian adults

2016· article· en· W2319438057 on OpenAlexaffvenueabout
Sarah Colatruglio, Joyce Slater

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPerspective (graphical)Grounded theoryPerceptionLiteracyQualitative researchFace (sociological concept)PsychologyIndependent livingGerontologyDevelopmental psychologySociologyPedagogyMedicineSocial scienceComputer science

Abstract

fetched live from OpenAlex

The purpose of this qualitative, grounded theory study was to explore the concept of food literacy from the perspective of young Canadian adults who recently transitioned to independent living. Seventeen individual, in-depth interviews were conducted with Canadian university students who recently transitioned to independent living. Results suggest that young adults face significant challenges with regard to healthy eating and acquiring and utilizing food literacy. The main reasons for these challenges were due to a lack of food and nutrition education prior to independent living through home and school environments, time-constraints, and complex food relationships. This study will add to the existing body of literature by exploring the food experiences of young adults and the concept of food literacy from their perceptions, thereby strengthening theoretical foundations and developing practical recommendations moving forward.

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)0.003
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 score0.038
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0120.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.076
GPT teacher head0.264
Teacher spread0.189 · 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

Citations47
Published2016
Admission routes3
Has abstractyes

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