The attraction of public-private partnerships for road construction in India, as affected by both positive and negative factors
Bibliographic record
Abstract
The paper aims to pinpoint and assess the perceived advantages and disadvantages of the Public-Private Partnership (PPP) for road development in India. Main PPP project contributors in Indian PPP road projects were polled via questionnaire. A literature review was used to select fifteen favourable characteristics and thirteen unfavourable factors for the questionnaire. Descriptive statistical analysis is used to analyze the data that was collected. The elimination of government financial restraints, project cost and time management, the reduction of government funds committed to capital investment, improved maintainability and accelerated project development are the key positive characteristics that draw PPP in Indian road projects. Excessive participation restrictions, protracted negotiating delays, ambiguity surrounding government objectives and evaluation standards, a lack of employment possibilities, and a lack of experience and the necessary skills make PPP undesirable. Both the public and private sectors can benefit from PPP in various ways. All sectors must make decisions based on proper assessment criteria during the project development stage. The decision-makers of PPP projects benefit early on from thoroughly understanding both positive and negative elements.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".