Canada in an Era of Intelligent Machines: How Institutions Condition Knowledge Generation and Innovation in a Learning Economy
Bibliographic record
Abstract
This dissertation project develops a human-centered approach to innovation founded on the competence of people and firms. It seeks to establish a link between learning and innovative capacity by examining two industrial sectors crucial to the Canadian economy: accounting professional services and auto parts manufacturing. While the contours of the emerging AI-driven economy remain uncertain, as digitization gains momentum and intersects with the imperative of transitioning to a post-carbon economy, it is clear that economic value is increasingly derived from the contribution of intangibles – software, large scale databases, and intellectual property that embody human knowledge and ingenuity. Results provide evidence to challenge the prevailing innovation policy regime that assumes investments in research and development alone are adequate to ensure the transition to the AI-driven technological paradigm. A key contribution of this study is to provide an institutional account for automation-driven labour market bifurcation. A theoretical framework combining Historical Institutionalism and Schumpeterian inspired evolutionary economics explains why the dynamic concept of a ‘learning economy’ is superior to the static conception of a knowledge economy to explain the effect of automation on labour markets. Empirical research examines labour market policy related specifically to skills development and continuing education aimed a working age adults popularly conceived as ‘lifelong learning’. Findings reveal that policy choices are the outcome of intrinsic socio-political forces unique to each industrial sector producing workforce development trajectories that are critical to understanding Canada’s lacklustre innovation performance.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".