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Record W3037139059 · doi:10.5539/ies.v13n7p145

Creative Economy Teaching and Learning–A Collaborative Online International Learning Case

2020· article· en· W3037139059 on OpenAlexvenueno aff
Aparna Katre

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
FundersOcean University of ChinaUniversity of Minnesota DuluthUniversity of Minnesota
KeywordsMindsetHigher educationSociologyPedagogyCreativityCollaborative learningCultural diversityBlended learningLeverage (statistics)Knowledge managementPublic relationsEducational technologyPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Culture plays a central role in the creative economy, not only in terms of developing creative products and services but also in terms of shaping the processes by which products are crafted. Among various pedagogical approaches for the development of creative products, Collaborative Online International Learning (COIL) emerges as a promising vehicle. Educators can leverage audio, visual, and written communications technologies to connect learners from geographically distant cultures and place culture at the center of the creative product development processes. The University of Minnesota Duluth’s introductory class on cultural entrepreneurship, CUE 1001, hosted a semester-long COIL project with Ocean University of China’s Cultural Industries Management program to facilitate such innovation in cross-cultural teams. An ex-post evaluation of the project suggests that learners can appreciate the overall significance of culture when conceptualizing creative services and products. They develop an intercultural mindset and acquire the tools to work effectively in cross-cultural settings. Institutions of higher education can leverage COIL in a variety of domains, while studies comparing traditional and COIL-based approaches can further add to the body of knowledge regarding intercultural awareness and the internationalization of learning in higher education.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.788
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0000.000

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 head0.041
GPT teacher head0.342
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations23
Published2020
Admission routes1
Has abstractyes

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