The performance of transparency in public–private infrastructure project governance: The politics of documentary practices
Bibliographic record
Abstract
That public–private infrastructure partnerships (P3s) present problems in relation to democratic accountability has often been noted, with calls for greater transparency often following. Such calls tend to assume that anything that promotes transparency will further accountability and openness. Drawing on socio-legal studies of the documentary and other information practices that underpin and operationalise governance, this article carefully examines the features and the possible uses of the documentation that is made public by the PPP sector, in Canada. We find that information practices that perform and produce transparency (such as posting project documents online) may produce a merely illusory accountability. Particular attention is paid to the scale at which infrastructure planning information is made public, the selection of content included in the documents (e.g. photos of buildings versus background information), and the information formats commonly utilised. Overall, we find that the information that is made public does not actually empower the concerned public: projects are presented out of context, devoid of historical or comparative context and without reference to any broader regional or other plan, and when ‘real’ documents are made public, neither the content nor their framing enables effective openness, thus hindering accountability.
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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 | 0.109 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.018 | 0.053 |
| Scholarly communication | 0.032 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".