Bibliographic record
Abstract
In the news and ever present on the minds of people in the technology and manufacturing sector is Canada’s need to replace nearly four hundred thousand skilled employees who will be progressively retiring over the next several years. To compound this problem, there has been a steady decline in student enrollment in the secondary and post secondary institutions in the science and technology areas (one of the main human resource feeds). Part of the reason for this can be directly related to a lack of awareness, not only to the diverse and exciting career opportunities found in contemporary manufacturing, but also just how important this sector of the economy will be to Canada’s future. This paper explores the success of the CME (Canadian Manufacturers and Exporters) pilot discovery program initiated by Design Engineering from the University of Manitoba. The initiative was directly aimed at secondary students from science, math, business, art, and technology programs. The main objective was to inspire these students by allowing them to experience first hand the many exciting facets of modern-day manufacturing. This was achieved by a hands-on approach to designing, building, and testing a project using their ingenuity to problem solve and the latest type of technology to fabricate their design. The success of this program was at the very least to be aware of a career option they may not of otherwise considered, and at the very most an opportunity to take the first steps to an exciting and rewarding career with the many possibilities that manufacturing has to offer.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.130 | 0.015 |
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".