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Record W3113896925 · doi:10.1080/1554480x.2020.1860060

Identity texts: an intervention to internationalise the classroom

2020· article· en· W3113896925 on OpenAlexaffabout
Rahat Zaidi, Dania El Chaar

Bibliographic record

VenuePedagogies An International Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCognitive dissonanceIdentity (music)Sociocultural evolutionCultural identityPedagogyPsychologyIntervention (counseling)HumanitySociologySocial psychologyAestheticsAnthropologyPolitical scienceArt

Abstract

fetched live from OpenAlex

The growing number of international students studying at Canadian universities has exacerbated the need to address identity, cultural aspects of teaching, and the commonalities of different cultures through a transcultural lens. To explore these concepts, researchers conducted a qualitative study using a workshop format at a large university in western Canada with graduate students, postdoctoral students, and faculty members from multiethnic backgrounds (N = 9). Two questions were posed to precipitate the research: 1) What does being transcultural mean to you? 2) Have you experienced cultural dissonance as part of your professional life? In a series of three activities, participants explored how to use identity texts (written, spoken, visual, musical, or multimodal sociocultural artefacts produced by participants) as an intervention to foster transculturalism and reduce tension and dissonance in a cross-cultural educational setting. Results indicated that using identity texts increased self-awareness, built trust, enhanced belonging, and revealed common humanity, thus creating opportunities to develop a successful professional identity in a multiethnic milieu.

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 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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.999

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 communication0.0020.003
Open science0.0030.000
Research integrity0.0000.000
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 head0.128
GPT teacher head0.457
Teacher spread0.328 · 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 teacher head, not a consensus.

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

Citations8
Published2020
Admission routes2
Has abstractyes

Explore more

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