Technology and Instrument Constituencies as Agents of Innovation: Sustainability Transitions and the Governance of Urban Transport
Bibliographic record
Abstract
Sustainable urban transport is a complex challenge requiring innovation in technologies, culture, and policies. Given the systemic nature of the issues involved, numerous studies have applied the transitions approach to urban transport. However, relatively weak conceptualization of agency in the transitions literature limits the usefulness of this approach for the governance of urban transport. The objective of this study is to contribute to the conceptualization of agency in the multilevel perspective to sustainability transitions. We propose that two types of actors exercise agency to foster innovation: technology constituencies, who promote the adoption of specific technologies by citizens, businesses, or governments; and instrument constituencies, who promote the adoption of specific policy instruments. In focusing predominantly on technological innovation, the transitions literature has generally juxtaposed these constituencies or considered them to be the same. We posit that the two constitute distinct, albeit possibly overlapping, actors and that their relationship(s) help better understand and explain how transitions evolve. We discuss the implications of this distinction for the governance of urban transport and argue that the presence of instrument and technology constituencies, and their relationship(s), should be examined empirically in future research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".