Invisible actors: Understanding the micro‐activities of public sector employees in the development of public–private partnerships in the United States
Bibliographic record
Abstract
Abstract Public–private partnerships (P3s) have continued to grow in importance both as an alternative infrastructure delivery model and a management practice in many jurisdictions and across institutional contexts. This study draws on theinstitutional workperspective to investigate the nature and form of micro‐activities that interact to cement a policy through the examination of the implementation of P3s as a management practice. The study advances our understanding of how the micro‐activities of governmental agents and actors affect the development, codification, and support structures of P3s. We adopt a multi‐location, field‐based case approach and a diversified institutional setting presented by P3‐adopting regions in the United States. We deploy an institutional work perspective to identify the micro‐activities that are undertaken as part of the P3 routinisation and acceptance process. Following Perkmann and Spicer's classification, and consistent with the concept of institutional work (Lawrence & Suddaby), we find that the ordinary day‐to‐day micro‐activities undertaken by government employees, categorised aspolitical work,technical work, andcultural work, are structurally, strategically, and intentionally deployed to achieve the successful implementation of P3s. In addition, we uncover a complementary managerial micro‐activity undertaken alongside institutional work, what we termorganisational restructuring, as part of the composite of activities that facilitate a successful P3 implementation. We suggest that the nature, extent, and impact of ordinary government employee micro‐activities on the implementation and acceptance of P3s deserve further empirical inquiry.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".