Towards Performance Measures of Transportation Networks in Nigeria: Lessons from the Developed Countries
Bibliographic record
Abstract
Transport is very key and important in the socio- economic transformation of any region or country. It is the life wire upon which other socio-economic activities of any nation depend. Be that as it may, government at all levels are always anxious to provide efficient transport services to meet the ever increasing desire of man to move freely while pursing his day to day activities. In the developed world, attention is shifting to the evaluation of transportation infrastructures as distinct from mere provision of such facilities. Hence, this study examines the various efforts of government of some developed countries of the world in their bid to make sure that transportation networks provision not only exist in some localities, but perform the roles they are meant to perform. Data for this study was collected from the secondary source through a review of existing literatures on how countries such as the United States, the United Kingdom, Canada Germany etc. have been able to apply performance measures to their transportation system; thereby giving room for a near perfect and efficient transportation system. Content analysis was used to analyze data. Findings revealed that no serious agencies of government in Nigeria had been put in place at both the federal and state levels to co-ordinate and promote performance measures of transportation networks. The study recommended the establishment of agencies that can get feedback from the populace, the performance level of transportation networks and infrastructures in Nigeria for improved transportation system in the country. Keywords: Performance, Transportation, Networks and Lessons).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".