Branding, defining and belonging: Creating an identity for the Engineering Communication Program
Bibliographic record
Abstract
In 2010, the Engineering Communication Program (ECP) teamed up with Engineering Strategic Communication to find a visual way to resolve ECP's identity issues. Not only was the program a kind of outsider to the world of math, hard science and research which were identified with University of Toronto Engineering, but the instructors themselves had a difficult time differentiating themselves from Teaching Assistants. Students did not have a conventional way to identify instructors who taught interactively in small classes scheduled during tutoring times or who tutored in one-on-one or one-on-team settings. The situation was further complicated by the fact that the university had recently completed a branding exercise and was centrally determining what kinds of symbolic representations or typefaces could be used. We wanted to utilize the university new “brand” as well as our own faculty's in order to represent ourselves visibly as part of the engineering world. Our answer, within what was currently permitted by the university, had two components: a type treatment of our name, matching the type treatments used by engineering departments, and an insignia that introduced an acronym for communication instructors - MyCI. In future, we will be conducting a study to determine the associations the insignia evokes for students.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".