Primary influential factors in the management of public transportation projects in Taiwan
Bibliographic record
Abstract
The primary influential factors must be screened out to improve the efficiency of management of public projects. This study synthesizes 91 possible influential factors in the management of transportation projects in Taiwan. Following the procedures of the multicriteria evaluation method and the fuzzy analytic hierarchy process (FAHP), the primary influential factors are screened out and ranked in order of priority. After analysis, it was found that the top five primary influential factors for responsible entities are (i) introduction of the earned value analysis, (ii) efficiency of the geotechnical survey, (iii) environmental laws and regulations of the local government, (iv) price-index fluctuation, and (v) on-site safety management. The top five factors ranked by the supervisory entities are (i) manpower, (ii) revision of laws and regulations, (iii) price-index fluctuation, (iv) traffic conditions, and (v) mistakes or faults of design. In the contractor phase, the top five factors are (i) bidding price, (ii) scope of the contractor, (iii) price-index fluctuation, (iv) management education and training system, and (v) government procurement act.Key words: management, influential factors, transportation project, fuzzy analytic hierarchy process (FAHP), Taiwan.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".