The governance of major public infrastructure projects: the process of translation
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the process of translation of an institutionalized governance framework as adapted to a major project in practice. Although infrastructure projects have been studied for decades, most studies have emphasized economic or contingency-based perspectives. Of those studies, some researchers have focused on governance frameworks for public infrastructure projects, and their impact for shaping the front-end phase of those projects. Yet, little is known about the way actors translate and enact those governance frameworks into practice. Understanding this translation process will lead to a better understanding of the overall performance of major infrastructure projects. Design/methodology/approach This qualitative research is based on a case study of one public infrastructure project in the health sector in Quebec, Canada. Through non-participant observation and interviews, the planning phase of the project is presented as it unfolds. Findings The process of translation is presented, from the ostensive, institutionalized governance framework, to appropriation into performative practices, which resulted in 12 specific practices: four “structuring” practices at the institutional level, five “normalizing” practices at the organizational level and three “facilitating” practices at the project level. Originality/value The main contribution of this paper is to enrich our understanding of the governance of major public infrastructure projects with process- and practice-based theories.
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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 | 0.086 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".