RE-CONCEPTUALIZING ENGINEERING COMMUNICATION USING AGILITY AND EFFICACY
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".