Industry Forum III: Towards A Common Language
Bibliographic record
Abstract
Engineering Education literature acknowledgesthat the language Academia uses to assess the abilities ofengineering students may not be the same as the languageIndustry uses to measure the abilities of new graduates at thetime they enter the work force. It also suggests that theunderstanding and expectations of Industry may differ fromAcademia. If the language, perceptions and expectations aredifferent, so too could be Industry’s assessment of theknowledge, skills and attitudes of new engineering graduates.Consequently, Industry may need to spend additional resourcesto develop the abilities of new hires to meet their own needs.The Industry Forum III was conducted in partnership withmembers of Manitoba Industry and members of Academia fromthe Faculty of Engineering at the University of Manitoba withthe objective to develop a common language that Industry andAcademia can use in concert to measure the abilities of newengineering graduates. This paper details the findings from theforum, as well as the changes made to the University ofManitoba graduate attribute rubrics in the pursuit of a commonlanguage for our engineering stakeholders.
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 | 0.107 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".