Interpretive Flexibility in Technology Innovation Systems and Policy Mixes: The Case of Smart Grids
Bibliographic record
Abstract
In technology innovation system (TIS) and policy mix literatures, the interpretive flexibility of emerging technologies allows for the pursuit of multiple pathways. For a pathway to be realized, structural tensions between the emerging technology and the larger sociotechnical system within which it is being embedded must be resolved, such as through novel combinations of complementary technologies and institutions. Alternatively, stagnation may occur in the event that structural tensions cannot be resolved. What is not yet well understood in these literatures are the interpretive dynamics and interactions between a TIS and its broader environment in the context of multiple TIS pathways in development. To understand these dynamics and interactions, our research question asks how to integrate a micro-level examination of TIS pathway construction within sociotechnical transition and policy mix frameworks. We address this question through use of a social construction of technology and society model to identify interpretative frames and stakeholder problem-solution pathways within a regional TIS. Our qualitative case study focuses on development of smart grids in the Maritime region of Canada. The central novel contribution of our paper is our adaption of the social construction of technology framework and its synthesis with perspectives on TIS, transitions and policy mixes.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.069 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| 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".