A Multi-Criteria Prioritization Framework (MCPF) to Assess Infrastructure Sustainability Objectives
Bibliographic record
Abstract
This paper presents a Multi-Criteria Prioritization Framework (MCPF) that can assist decision-makers and government administrators in identifying and ranking infrastructure sustainability objectives in developing countries. The framework also helps governments of developing countries in assessing the priority of repair of damaged infrastructure assets, based on significant sustainability objectives. A Template of infrastructure sustainability objectives is developed through literature review and interviews with key experts. A questionnaire-based survey solicits experts’ opinions to rate the sustainability objectives based on their relative importance to the public, using a five-point Likert rating scale. The quality of experts participating in the rating process is determined using the pair-wise comparison method of the analytical hierarchical process (AHP) that calculates a crisp importance weight value of each expert, based on his or her qualification criteria. The relative importance index (RII) method is adapted to prioritize the sustainability objectives, which integrates the rating scores assigned by experts and their relative importance weight factors. A crisp facility sustainability priority index (FSPI) is computed using a survey-based approach and a weighted sum technique in multi-criteria decision analysis that determines the priority of repair of damaged infrastructure facilities, based on significant sustainability objectives. In order to test the applicability of the prioritization framework, a case study is applied in Egypt to demonstrate how the model can assist governments of developing countries in prioritizing damaged infrastructure assets that need urgent repairs. The prioritization framework presented in this paper offers a simple yet efficient evaluation technique to decision-makers with limited budgets that accounts for sustainability objectives in deciding on the repair priorities of damaged facilities.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".