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Record W3158243794 · doi:10.24908/iqurcp.8355

Public Stewards or Hired Guns?: An Inquiry into Engineering Education

2016· article· en· W3158243794 on OpenAlexvenueno aff
Lindsay Wiginton, Amy Buitenhuis

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsEngineering ethicsPoliticsPower (physics)Environmental ethicsEngineering educationSociologyUnrestScience educationPublic relationsLazinessSocial engineering (security)Political scienceSocial scienceEngineeringPedagogyLaw

Abstract

fetched live from OpenAlex

The world is facing converging crises of overpopulation and urbanization, resource depletion and globalwarming, political tension and unrest. Often, we call upon engineers, as technical experts, to addressthese issues. Engineers are allotted a large amount of decision‐making influence and power based ontheir assumed knowledge and skill sets. However, there is perhaps a danger in giving influence toindividuals with a limited understanding of social, political and environmental issues. In the words of Donna Riley, “The profession of engineering…has historically served the status quo, feeding an ever‐expanding materialistic and militaristic culture, remaining relatively unresponsive to public concerns, and without significant pressure for change from within” (Riley, 2008). This inquiry seeks to ask: “Are engineers truly prepared to tackle today’s contemporary issues?” by exploring the nature of the engineering industry in the 21st century, engineering education broadly, and the Applied Science Curriculum at Queen’s University specifically. It will draw on both independent research and focus groups with Applied Science students. Particular themes which will be explored include the engineering design process, the understanding of power relationships and the need for interdisciplinarity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.352
Teacher spread0.219 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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