Building walls within walls: Making value defensible in Public Private Partnerships
Bibliographic record
Abstract
Despite widespread criticisms, governments around the world have adopted Value for Money (VfM) analysis as a key metric in gauging the prospective value of infrastructure projects. This paper examines the institutional processes through which VfM is rendered defensible as a form of valuation. Drawing on a case study of Infrastructure Ontario in Canada, the paper demonstrates that this involves strategies for partitioning space and time, including boundary work, objectification, phasing and outsourcing. We argue that the institution of delays and distances ultimately fosters the displacement of subjectivity in the valuation process while entrenching distinctively financialized understandings of value. Moreover, we demonstrate that this is driven not so much by the organization's desire to control the future but by a defensive orientation that sets out to ward off potential critiques that may arise from project failures.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.056 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| 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".