Combining Creative and Technical Elements to Support the Transition of Canada’s Economy
Bibliographic record
Abstract
Canada is attempting to transition from an industrial society – one focused on natural resources and manufacturing – to an economy where knowledge and skills related to a new digital information future are paramount for success. Policymakers, particularly in the training and education space, recognize that adjustments must be made for Canada to truly excel in the new economy. The focus for educating the nation’s future workforce has come to be on enhancing the training received in grade-schools in four key subjects: Science, Technology, Engineering and Math (STEM). Additionally, the recognition that girls are traditionally underrepresented in these subjects had led to a concerted effort to attract girls to STEM subjects. The story told in this capstone catalogues how successful these policy efforts have been and showcases how the federal government, through its Ministry of Innovation, Science and Economic Development (ISED), has become a significant player in preparing Canadians for future opportunities. While the indirect involvement of ISED, through its funding of STEM education programs, has done a solid job of supplementing the youth-focused efforts of provincial education ministries, the limits on federal influence appear to have been reached. The author contends that the transitional change ISED is seeking may be found in a range of creative modifications to public policies. The recommendations brought forward may seem to be an unconventional take on the path that Canada needs to tread toward future prosperity; however, history suggests, and the author concurs, that inventive and even eccentric deliberation is required to address social challenges, particularly one as entrenched as gender disparity generally, and its effects in STEM uptake specifically. The recommendations in this capstone present an unorthodox policy solution to the complex problem of how to adapt education to the digital economy, making the argument that a mix of both creative and technical skills is necessary to secure Canada’s future prosperity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".