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Record W1980450965 · doi:10.1109/fie.2010.5673391

Work in progress — Shifting contexts: Investigating identity transformation in undergraduate engineering students

2010· article· en· W1980450965 on OpenAlexaff
Annette Berndt, Carla Paterson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThematic analysisEngineering educationAccreditationContext (archaeology)Identity (music)Set (abstract data type)Work (physics)Engineering ethicsEmpathyPedagogySociologyPsychologyEngineeringComputer scienceMedical educationQualitative researchEngineering managementSocial psychologySocial science

Abstract

fetched live from OpenAlex

To inform curricular change in accordance with accreditation criteria, interviews were conducted with engineering students currently active in Engineers Without Borders to ascertain why some engineering students are more interested in the social context of engineering than others. A thematic content analysis of interview transcripts revealed two sets of results: common personal characteristics among interviewees that led to the development of a capacity for empathy and recommendations for curricular change. A critical discourse analysis adds a third set of results that focus on interviewee references to “the self” and “the other.” Preliminary findings indicate that engineering students require co-/curricular opportunities that enable them to identify with “the other” facilitated by shifts in social context. It is argued that student engagement with shifting contexts contributes to identity transformation from technical problem-solver to “global engineer.”

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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 designQualitative
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".

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Citations1
Published2010
Admission routes1
Has abstractyes

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