The attractiveness of public-private partnership for road projects in India
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 objective of this paper was to have a study on the perceptions of stakeholders of Public-Private Partnership (PPP) projects for factors affecting the attractiveness of road projects in India. A questionnaire survey was conducted among major PPP project participants of Indian PPP road projects. Fifteen attractive factors were shortlisted through a literature survey for designing the questionnaire. Collected data was analyzed with factor analysis and descriptive statistical analysis. The findings resulted in three components: effectiveness of the private sector, effective time and cost management, and the public sector’s economic benefit. Eight factors were identified as highly affecting the attractiveness of PPP in Indian road projects. PPP provides ample diversity of net benefits to both the public and private sectors. During the project development stage, both sectors have to formulate decisions based on appropriate assessment criteria. Therefore, the reflection of attractive factors will assist the public-sector to select PPP in the road sector. It also helps to establish the strategy for road projects using PPP.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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