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Record W2070896297 · doi:10.1177/0741713614549231

Growing Everyday Multiculturalism

2014· article· en· W2070896297 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAdult Education Quarterly · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMulticulturalismSociologyRhetoricNegotiationDiversity (politics)Intercultural learningEnvironmental ethicsPedagogySocial scienceAnthropologyLinguistics

Abstract

fetched live from OpenAlex

While official rhetoric of multiculturalism claims to value cultural diversity, everyday multiculturalism focuses on how people of diverse cultural backgrounds live together in their everyday lives. Research on everyday multiculturalism has documented ways through which people negotiate senses, sensibilities, emotionality, and relationality across intercultural contact zones. While recognizing the importance of human intentionality and community in conditioning coexistence, this article also points to the constitutive power of practice-based learning that emerges through the coming together of human and nonhuman beings. Drawing on a qualitative study of the learning experiences of six Chinese immigrants in community gardens on a university campus in Canada, this article shows three ways of learning that foster knowing, connecting, and hybrid knowledge production across cultures: (a) learning through communities of conviviality; (b) learning mediated through nonhuman things such as land, waste, and free-floating seeds; and (c) learning through assemblages that fold in culture, place, and space.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.374
Teacher spread0.350 · 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