Minding the Gap Between Promise and Performance: The Ontario Liberal Government's Research and Innovation Policy, 2003-2011
Bibliographic record
Abstract
The Ontario Liberal government committed an unprecedented $3 billion between 2003 and 2011 to support research and innovation in order to drive economic growth and prosperity. Premier Dalton McGuinty established Ontario's first Ministry of Research and Innovation and appointed himself the inaugural minister, instantaneously pushing research and innovation to the top of the political agenda. Given the scale of new resources committed, Ontario's research and innovation actors intensified efforts to influence the development of research and innovation policy and secure their share of research and innovation money. This study examines the dynamism of policy development in a sector with a state that demonstrated a strong political will to advance an innovation-oriented agenda, and different groups of societal actors intent on influencing the development of that agenda and its supporting policies and initiatives. Using policy network analysis as an explanatory framework and bearing in mind the power of ideas on policy outcomes, this study explains why Ontario's Ministry of Research and Innovation policy developed the way that it did. The dissertation contends that various research and innovation actors played critical roles in ensuring that the ministry's suite of support programs maintained a supply-side innovation orientation between 2003 and 2007. However, changes in the policy network post-2008 made decision-makers more receptive to demand-side innovation programmatic ideas. And while the government introduced a small number of demand-side innovation initiatives to address recessionary concerns, Ontario's policy mix maintained a supply-side innovation bias. The dissertation identifies the factors that constrained a shift to a more demand-side innovation policy orientation, discusses the adverse impact these policy choices had on the government's efforts to realize its economic goals, and offers policy recommendations on how decision-makers can move forward towards implementing a strategic, integrated, place-based approach to policy development that will drive growth and sustainability.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".