Gouvernance et imputabilité : la protection des valeurs publiques à l'ère de la privatisation des services d'eau
Bibliographic record
Abstract
The international year of fresh water represents the opportunity to look back at the seemingly irresistible movement toward privatization and at the devolution of State responsibilities in water and wastewater services. The welfare State appears to be increasingly ill-adapted to times dominated by globalisation and efficiency, while the market and the private corporations are presented as a panacea for solving the water crisis. But expectations of the market have not been met. This thesis analyses the debate over privatization of these services and the fondamental impacts on public values of introducing a market philosophy into this industry. Rather than witnessing a retreat of the State, its role is evolving although direct service provision is superseded by heavy regulation of the industry. In criticizing the neoclassical approach to the public good and regulation, the thesis argues that individualizing the process of valuing the public good fosters a culture of conflict and complexity that ultimately undermines our ability to formulate and achieve common goals. This creates an important accountability deficit. The need for environmental efficiency and democracy in an era of uncertainty requires that we search for means of expanding the reach of public values and thus suggests an even deeper reshaping of our governance structures, public and private.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".