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Record W2604605355 · doi:10.24908/pceea.v0i0.4893

RE-CONCEPTUALIZING ENGINEERING COMMUNICATION USING AGILITY AND EFFICACY

2013· article· en· W2604605355 on OpenAlexfundvenueno aff
Penny Kinnear

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsMultidisciplinary approachDilemmaPerspective (graphical)Computer sciencePerceptionKnowledge managementEngineering ethicsPsychologyEngineeringSociologyEpistemologyArtificial intelligence

Abstract

fetched live from OpenAlex

Instruction in engineering communication, especially for students for whom English is an additional language, is often based on assumptions that the relationship between knowledge about language and the ability to use language is one of cause and effect. However, this perspective does not adequately encompass the complex, multidisciplinary nature of teaching, learning and practicing engineering communication. Partly in response to this dilemma I propose two concepts, agility and efficacy, for consideration in researching and teaching engineering communication. The concepts emerged from current conceptualizations of language as a distributed activity with a focus on the strategies, norms, perceptions, and material and symbolic means used to establish shared understanding and shared goals. Approaching this dilemma from an activity theory perspective provides an opportunity to take advantage of this multidisciplinary nature, especially with a consideration of shared objects. Activity theory is introduced through examples of engineering communication education and a multidisciplinary research project is proposed to identify the contradictions and conflicts that make teaching, learning and practicing engineering communication so challenging.

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)0.006
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 score0.292
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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
Published2013
Admission routes2
Has abstractyes

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