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Record W4380538028 · doi:10.3991/ijep.v13i4.38477

Proposing a Multi-Stakeholder Lens to Examine Global Community-Based Design Projects

2023· article· en· W4380538028 on OpenAlexaffabout
Libby Osgood, Nick Landrigan, Wayne Peters

Bibliographic record

VenueInternational Journal of Engineering Pedagogy (iJEP) · 2023
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsStakeholderKenyaStakeholder analysisPublic relationsBusinessKnowledge managementEngineeringPolitical scienceComputer science

Abstract

fetched live from OpenAlex

One implementation of global, community-based, engineering-student design projects invites students to practice design from a distance. Though it may not be possible to bring an entire engineering design class to the international location for students and various stakeholders to interact, a meaningful global experience can be educational and beneficial for all stakeholders. Recognizing that the impact of community-based projects extends beyond the students to numerous stakeholders, this paper proposes a multi-stakeholder lens which examines the roles, interactions, motivations, and responsibilities of stakeholders in a global, community-based design project. The lens was developed in part by a case study of a global design project connecting a first-year Canadian engineering design course, a rural Kenyan preschool, a non-profit organization, and additional Kenyan and Canadian stakeholders. Written by three of the stakeholders in the case study, the course instructor, a Canada-based community partner, and a design student, this paper concludes with recommendations on how to incorporate global projects in a domestic setting. Ultimately, adopting a multi-stakeholder lens transitions a myopic student-centric focus to an inclusive experience for all stakeholders, creating partners in the design and achieving a greater set of objectives.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.136
GPT teacher head0.342
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes2
Has abstractyes

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