Optimizing Physical Planning in the Developing Countries – A Case Study of Ondo State, Nigeria
Bibliographic record
Abstract
A new Ministry of Physical Planning and Urban Development has just been established in Ondo State, Nigeria. The development was expected to herald efficient and sustainable physical planning. To maximize the opportunities offered by this innovative development, some conditions are desirable. This paper therefore examined the problems confronting physical planning in the State and proffer appropriate solutions to unravel them with the intention of optimizing the gains arising from this innovation. The study involved a survey of physical planning mechanisms and agencies of governments responsible for physical planning in Ondo State. It investigated the evolution of physical planning in the state. Other variables examined include: human and financial capacity available for physical planning, development control process, master planning, inventory of project vehicles and equipment among others. The problems that were identified include lack of urban development policy, ineffective development control, inadequate/absence of capacity in appropriate discipline, dearth of spatial information and data and absence of master plan to guide the development of settlements in the State. The paper therefore canvassed for immediate evolvement of urban development policy coupled with series of legislations and regulations that would facilitate development control. Other recommendations include capacity building in relevant discipline, recruitment of staff with contemporary knowledge in urban planning, acquisition of spatial information and data for planning purposes and the immediate development of master plans for major settlements in the State.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".