Emerging stronger: addressing the skills under-utilization challenge for the future of work in Canada
Bibliographic record
Abstract
A thriving knowledge economy inherently relies on a highly skilled and adaptable workforce. And, as the [Coronavirus Disease 2019] COVID-19 pandemic has revealed, knowledge-intensive sectors are far more resilient in times of disruption than most other sectors. So what does it take to build the workforce that will help Canada transition to a knowledge economy? Despite a strong post-secondary system combined with robust labour market development programs, industry points to a lack of the right talent as a critical inhibitor for their growth. The enduring and frustrating irony to this challenge is that while Canadian [information and communications technology] ICT firms are struggling to hire business talent, significant numbers of workers in the Canadian labour market have the foundational skills to succeed in these roles. This paper sets out to explore this gap, beginning to answer these critical questions: How do we ensure that opportunities are maximized for Canadians in the transformation to a knowledge-intensive economy? What are the appropriate support mechanisms for mid- to late-career workers who are most in danger of being displaced during this transformation? How should Canada's skilling and education systems evolve to prepare those entering the workforce in an environment where skills needs are constantly evolving? Most importantly, it asks the fundamental question: Is the problem really mismatched skills? To answer this, the authors go back to first principles and ask: What does it take to land a job in tech, especially if you have the skills but are coming out of a totally different sector?
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".