An Integrated Approach for Prioritizing Projects for Implementation Using AHP
Bibliographic record
Abstract
This paper presents the Analytic Hierarchy Process (AHP) as a potential decision making method for prioritizing road projects for implementation.An examination of the way implementing agencies decide over which road project to select for execution reviews a constant desire to have a clear, objective and scientific criteria.However, decision making is, in its totality, a cognitive and mental process derived from the most possible adequate selection based on tangible and intangible criteria, which are arbitrarily chosen by those who make the decisions.In this paper, a hierarchical structure is constructed with data from a regional road directorate's scheduled potential roads for implementation based on commonly known factors used by agencies for selecting projects.An integrated factor base (IFB) taking into consideration, the Social, Legal, Environments, Economic, Political and Technological (SLEEPT) influence of roads has been developed to aid in providing a systematic approach for prioritizing road projects.By applying the AHP, candidate projects can be prioritized in descending-order of the most viable project to be selected for implementation.The paper shows the adequacy of the AHP and proposes the use of simplified professional software, the `Expert Choice' that is available commercially and designed for implementing AHP.It is hoped that this will encourage the application of the AHP by project officials and other project management professionals for implementing 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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".