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Record W4405551677 · doi:10.1386/9781835950364_7

Marvelling at the Ethnoverse: Intercultural Learning through Traditional Music

2024· book-chapter· en· W4405551677 on OpenAlexaff
Roger Mantie, Pedro Tironi

Bibliographic record

VenueIntellect eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychologyIntercultural learningPedagogy

Abstract

fetched live from OpenAlex

This chapter examines intercultural learning as it occurs at a series of music camps known as ‘Ethno’ gatherings. Data include post-hoc analysis of 114 interviews of participants conducted 2019-2021, and 14 interviews of Ethno organizers and artistic mentors conducted in spring 2021. Theoretically, the chapter is informed by Appadurai's (1990) concept of the ethnoscape, Appiah's (2010) writing on cosmopolitanism, and Allport's (1954) contact hypothesis (i.e., intergroup contact theory). Three themes are discussed: intergroup contact, artistic mentors, and culture as political. The chapter concludes that, despite their innumerable conceptual difficulties and contradictions, Ethno gatherings leave most participants with positive feelings about intercultural peace and harmony.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.161
GPT teacher head0.327
Teacher spread0.166 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2024
Admission routes1
Has abstractyes

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