OPERATION PERFORMANCE MEASUREMENT OF PUBLIC RENTAL HOUSING DELIVERY BY PPPS WITH FUZZY-AHP COMPREHENSIVE EVALUATION
Bibliographic record
Abstract
As governments promote greatly the Public Private Partnerships (PPPs) to develop the Public Rental Housing (PRH) projects, the effective and efficient operation performance measurement should be pivotal for ensuring the success and sustainable development of these projects. Thus, this paper investigated operation performance indicators (OPIs) and measured the performance level of PRH PPP projects by fuzzy-analytic hierarchy process (AHP) comprehensive evaluation (FACE) method. Four important aspects of PRH PPP projects related to the operation performance and an evaluation indicator system of 21 OPIs from these four aspects were developed, the weights of which were calculated by using the AHP method. Based on fuzzy mathematics and the expert evaluation method, all the OPIs were quantitatively graded according to five ranks of evaluation criteria. Membership functions, weights of OPIs, and maximum membership degree principle were utilized to establish a multi-level FACE model for operation performance measurement of PRH PPP projects. One PRH PPP project of Nanjing, Jiangsu Province in China was chosen as the case study. Evaluation results were derived from the proposed model, and they generally conform to the actual situation. This study provides an effective operation performance measurement framework for PRH PPPs projects.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".