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Record W7115591100 · doi:10.7771/1812-9129.1028

Student Group Work in Widely Interdisciplinary Teams

2025· article· en· W7115591100 on OpenAlexaff

Bibliographic record

VenueInternational journal on teaching and learning in higher education · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDivision of labourWork (physics)Value (mathematics)Group workGroup (periodic table)Set (abstract data type)

Abstract

fetched live from OpenAlex

Group work is often used in university courses. This article examines group work in a widely interdisciplinary holography course that combines both art and science, for students from the arts, humanities, social sciences, and sciences. In these interdisciplinary teams, how much specialization of labor (dividing work according to students’ pre-existing abilities or personal interests) is acceptable? We present student survey responses regarding their attitudes toward interdisciplinary group work, and their practices in dividing the work, to determine how much specialization of labor is taking place within the interdisciplinary teams. The surveys indicate a mix of approaches among groups concerning the division of labor based on prior skills. In the presence of specialization of labor, students learned from their partners and displayed a positive attitude toward working with someone from a different discipline. We believe that the intriguing nature of the holography projects helped many students avoid dividing the work according to their prior skills, and helped them see the value of working in a widely interdisciplinary team.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.042
GPT teacher head0.462
Teacher spread0.419 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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