Standard of Living & Quality of Life Relies on Innovation: Innovation Relies on Engineering Design
Bibliographic record
Abstract
Quality of life has advanced since the industrial revolution and this advancement has accelerated with the information revolution. Life expectancy has increased, catalytic converters protect our air, a disabled athlete runs with the fastest runners in the world1, and global real GDP per capita has grown by a factor of 2.5 over the past 50 years2. This quality of life advancement is the result of continuous innovation. In today’s global economy, innovation is essential for Canada to compete (even to participate) and to continue advancing our quality of life. Collective global innovation has never been more critical. World population growth (7 billion and counting), diminishing non-renewable resources (oil and beyond) and escalating environmental challenges (climate change and pollution) all require global scale innovations or our collective quality of life will not be sustained. Canadians have contributed much to the world including the telephone and smartphone, CANDU® reactors, snowmobiles, IMAX®, and the pacemaker. However, over the last number of years, there have been multiple reports critical of Canada’s capacity for technological innovation3 and studies that offer strategies for improvement.4 While it is true that innovation is essential to the future of both Canada and the world, innovation is only a means to an end and it is incumbent on us to define the desired ends. Innovation can be a means to a higher quality of life and a more sustainable future for generations to come or it can simply be a means to increase the financial prosperity of the nation. To achieve the ends we value, it is essential to measure innovation in terms of these ends, not in terms of subtle differences in the rate of change in the GDP per capita. Are our innovations leading to cleaner water for all, a healthier and complete diet for all, and meaningful employment for all?
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".