Manitoba Infrastructure and Transportation's Training and Recruitment Strategy: Building the Infrastructure to a Sustainable Workforce
Bibliographic record
Abstract
Manitoba Infrastructure and Transportation (MIT) is the government department responsible for managing and ensuring the long term sustainability of a diverse provincial infrastructure network with an estimated replacement value of over $10 billion. Of MIT's 2,500 staff, approximately 1,600 are located in the department's Engineering and Operations (E&O) division. A large percentage of these staff are engineers or in a related discipline and oversee the maintenance, repair and renewal of thousands of culverts, bridges, drainage ditches, 19,000 kilometres of roads and over 2,000 kilometres of winter roads on an annual basis. The E&O division has a high demand for professional and paraprofessional staff due to most of the engineering services being provided in-house, using methods based specifications. Historically this has sustained a strong expert and knowledgeable core of technical staff. This workforce is now reaching a critical stage due to retirements and a lack of staff with 10 to 15 years experience. To address shortages and changing demographics in Manitoba's workforce, and their impact specifically on MIT, the E&O division is developing unique and creative methods of training, developing, recruiting, and retaining the skilled employees needed today and into the future. This concept provides an aggressive and coordinated approach through presentations in high schools, post-secondary institutions, rural and northern communities, conferences, symposiums, and career/job fairs throughout the province.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".