A Bibliometrics Analysis of Canadian Electrical and Computer Engineering Institutions (1996-2006) Based on IEEE Journal Publications
Bibliographic record
Abstract
This paper presents a bibliometric assessment of Canadian institutions in the discipline of Electrical and Computer Engineering (ECE) from 1996 to 2006. The paper is a first step in identifying the top institutions over a 10-year period in Canada. The rankings are calculated using three metrics: (1) simple count of papers, (2) journal impact factors, and (3) journal impact factor of each institution normalized by its faculty size. The venues/journals considered are 71 flagship IEEE Transaction journals in different areas of ECE which are perceived as the most prestigious venues in the discipline. Using the three metrics, the top-ranked institutions are identified as: the University of Waterloo (by two metrics), and Queen’s University’s (by one metric). Our study also reveals other interesting results, such as: (1) Researchers from the universities of Waterloo and Toronto, combined, authored about a third of all the Canadian papers published in the IEEE Transaction journals during the time period under study (691 of 2,540 papers). (2) Canadian provinces have different levels of ECE research productivity and efficiency, and (3) ECE ranking of the Canadian institutions has similarities and differences versus a recently-published software engineering ranking of the same institutions. While this study is in the context of Canadian ECE institutions, our approach can be easily adapted to rank the institutions of any other nation and/or in any other discipline.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.103 | 0.195 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".