Accommodation of Interests of the State, Business and Civil Society in Environmental Projects Implemented Through Public Private Partnership in the Russian Federation
Bibliographic record
Abstract
Environmental projects have a number of distinctive features, among them an increased capital/output ratio, relatively high risks, lengthy payback periods, and outcomes that are hard to evaluate using financial indicators. Public private partnership (PPP) appears to be a viable approach for the implementation of such projects; however, existing mechanisms for the accommodation of long-term interest of the state, business and civil society are inadequate to ensure their success. In this context, the author presents an algorithm of multi-criteria analysis to evaluate the social efficiency of PPP-based environmental projects, which takes into account the impact of both financial and non-financial outcomes and includes crowdsourcing public opinion into the final decision-making process. Special priority is given to the assessment of multiplicative effects, as their role and impact on the feasibility of investment are often underestimated. The author’s conclusions and recommendations are illustrated using the case study of a construction project for a municipal solid waste processing facility. Full text available at: https://doi.org/10.22215/rera.v11i2.1191
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".