An Integrated Approach for Prioritizing Projects for Implementation Using AHP
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
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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