The Positional Logic of Technological Innovation in the Canadian Bioenergy Sector
Bibliographic record
Abstract
For almost fifty years, OECD governments have sought to increase the rate of technological innovation in the energy sector to resolve price-supply risks, improve economic performance, and address environmental concerns. Policy and innovation effort has been concentrated within the transportation, electricity, and industrial sectors; fewer resources have been allocated to support the innovation of alternative energy technologies applicable to non-industrial buildings. Small European nations and many subnational jurisdictions are important exceptions to this general tendency. This dissertation presents an integrative theoretical approach to understanding these patterns that begins explanation with the power-distributional effects of material reality and then considers the power-distributional effects of institutions that do not logically derive from material reality. The explanation is improved through attention to temporal processes (path dependence and positive feedback). Areas of the political economy that are prone to oligopoly and vertical/horizontal integration (that are ”centralized”) and that involve complex technologies are more likely to receive resource allocations owing to greater institutional development (organizations and rules). The development of institutions in areas of the political economy that are less centralized and associated with less complex technologies functions to expand the scope of policy and innovation effort. The approach is tested using the example of clean technology innovation in the Canadian bioenergy field. In Chapter 2, federal policy and sectoral developments since the 1970s demonstrate that the transport sector – a highly centralized area of the economy involving the innovation of complex technologies – has been the principal emphasis of policy and innovation effort. The lack of federal policy effort in the non-industrial heating sector is attributed to its decentralized and low-tech nature. In Chapter 3, a comparison of the development of recent non-industrial heating policies in the bioenergy fields of Ontario and Quebec provides evidence supporting the proposition that institutionally complex jurisdictions function to increase resource allocations in decentralized, low-tech areas of the economy. Chapter 4 presents a method of technical and policy model parameterization that is designed to mitigate overconfidence, a plausible mechanism of positive feedback. The final chapter summarizes the findings and discusses implications for clean energy transitions and comparative political economy scholarship.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".