The attraction of public-private partnerships for road construction in India, as affected by both positive and negative factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it