Understanding the interacting layers of multi-level governance in Quebec’s public infrastructure projects
Bibliographic record
Abstract
Purpose The purpose of this research is to analyze the interactions among different governance layers – metagovernance, governance of networks and network governance – within the governmental public infrastructure (GPI). Specifically, it focuses on the development and implementation of a collaboration performance indicator. The study aims to understand how this indicator can serve as a tool for enhancing governance processes and outcomes in complex public infrastructure projects, examining its influence on policy, strategy, and operational levels and assessing its impact on enhancing collaborative governance. Design/methodology/approach This study employs a mixed-method approach, integrating both action research (AR) and intervention research (IR). Over a span of five years, 12 cycles of AR were conducted to explore the dynamics of multi-level governance at a GPI. This methodological blend allows for in-depth analysis of both process and outcome, facilitating real-time adjustments and improvements in governance strategies. The research emphasizes the practical application of theoretical concepts to understand the interaction of governance layers in public infrastructure settings. Findings Preliminary findings indicate that the implementation of the collaboration performance indicator at a GPI has been successful, demonstrating effective metagovernance application and facilitating significant institutional changes. These changes, driven by high-level directives, reflect the adaptability and impact of governance networks. The study illustrates a nuanced balance between autonomous action and adherence to overarching governance strategies, termed as the equilibrium between Type I and Type II governance. Additionally, the research highlights the role of stakeholder interventions in adapting governance practices to evolving project needs and contexts. Originality/value This study provides novel insights into the application of multi-level governance in public infrastructure projects, particularly through the lens of a newly developed collaboration performance indicator. It showcases the original integration of AR and IR methods to study governance, offering a unique perspective on the dynamic interactions between different governance layers. The research highlights the value of such indicators in driving metasolidarity and fostering institutional change, contributing significantly to the theoretical and practical understanding of governance in complex project environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".