A comparative study on the manufacturing engineering education in Ontario universities.
Bibliographic record
Abstract
Manufacturing engineering plays an important role in the Canadian economy. However, progress made in improving the manufacturing engineering programs at the university level in Canada has fallen behind compared to what in other developed countries such as Germany, United States and Japan. In order to improve the competitiveness of the Canadian manufacturing industry, it is necessary to reinforce the manufacturing engineering education programs, especially the undergraduate programs. The objectives of this thesis are to find the limitations of the Canadian manufacturing engineering education programs and accordingly, to propose improvement. The thesis begins with a general review of some manufacturing engineering programs in other countries around the world. Then a study of the current status of manufacturing engineering education in Canada, with a focus on the undergraduate manufacturing engineering education in Ontario, is reported. The needs of current and future Canadian manufacturing industry and the functions of manufacturing engineers in the future are discussed. Finally, in order to solve the problem of the lack of hands-on experience in the existing programs, a proposal of a Manufacturing Engineering program with a Computer Integrated Manufacturing (CIM) Laboratory is presented.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .Z55. Source: Masters Abstracts International, Volume: 41-04, page: 1172. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.018 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".