MétaCan
Menu
Back to cohort
Record W3099018005 · doi:10.1080/23752696.2020.1841569

Foodways, community, and film-making: a case study of funds of knowledge in higher education

2020· article· en· W3099018005 on OpenAlexafffund
Mashael Alharbi, Yuen Sze Michelle Tan, C. Owen Lo

Bibliographic record

VenueHigher Education Pedagogies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsFoodwaysDialecticAcademic communityKnowledge creationLearning communitySociologyCommunity collegePedagogyMedical educationEngineeringSocial scienceMedicine

Abstract

fetched live from OpenAlex

Undergraduate students can develop different bodies of knowledge by engaging with members of their family, university, and community. In this study, we investigated how undergraduate students used the knowledge, skills, and resources they gained in class to engage with their local communities through the creation of documentary films as part of an undergraduate course assignment. We employed funds of knowledge as a theoretical lens to understand how students utilize their academic knowledge in community contexts, and how students accumulate funds of knowledge within these contexts. The findings demonstrate that community-based knowledge was transmitted through a food discourse, which in turn informed the students’ academic knowledge. What is highlighted is the dialectical relationship between knowledge developed through community engagement and those gained through academic contexts.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.016
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.010
Scholarly communication0.0070.005
Open science0.0020.007
Research integrity0.0030.004
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 head0.279
GPT teacher head0.387
Teacher spread0.109 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueHigher Education PedagogiesSame topicLiteracy, Media, and EducationFrench-language works237,207