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Record W4411957534 · doi:10.59236/td2013vol6iss31325

Mixing Business with Science

2013· article· en· W4411957534 on OpenAlexaff
Joan Flaherty, Mike von Massow

Bibliographic record

VenueTransformative Dialogues Teaching and Learning Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMixing (physics)BusinessPhysics

Abstract

fetched live from OpenAlex

Interdisciplinary studies are increasingly getting attention for their potential to add value to graduate education.This study elicited the views of graduate science students who had completed a pilot interdisciplinary course integrating their normal graduate studies with business knowledge.The students were asked for their perception of the course's learning outcomes and the pedagogy associated with those outcomes.Their responses suggest high level cognitive outcomes, beneficial to current studies and future careers: exposure to other perspectives; increased self-awareness; enhanced communication skills; and an understanding of how their research "fits" into the business world.Because interdisciplinarity requires that students venture into unknown territory, the recommended teaching-learning approaches attempt a balance between encouraging risk-taking and eliminating it altogether: foster student ownership; provide low-risk assignments and detailed feedback; avoid disciplinary bias; and focus on communications.

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)0.007
metaresearch head score (Gemma)0.015
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 score0.013
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0130.008
Open science0.0010.015
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0100.002

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 head0.006
GPT teacher head0.195
Teacher spread0.188 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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