Oral Interviews to Preserve the History of Engineering Accomplishments in Canada
Bibliographic record
Abstract
One of the missions of the Engineering Institute of Canada (EIC), an organization of 14 scientific member societies, is to promote the engineering profession through preserving Canadian engineering history and archives. The best way to achieve this objective is for individual engineers to record their achievements, but this has been challenging in practice. To address this challenge, the History and Archives Committee of EIC embarked on an oral history interview project in 2020 to record the achievements of senior engineers. To date, 11 female and 17 male engineers have been interviewed, and edited video recordings and transcripts of these interviews are now archived online. Four interviews are being repeated in French for some bilingual content.This paper describes some of the essential logistics of the process, including: the resources required; the means adopted to identify suitable interviewees, and the ethical constraints that must be followed. The interviews are based on a simple, 12-question guide that allows the interviewees, or "Narrators," the freedom to take the content of the interview to wherever they want it to go. The edited videos, which are approved by the Narrators before dissemination, are used to create 3-to 5-minute "snippets" to attract viewers to watch the full, 45-to 90-minute interview. Preliminary outcomes of the project are described, and recommendations for future oral history interviews are presented.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".