Bibliographic record
Abstract
The deplorable condition of neighbourhood facilities in Bauchi metropolis persists, while the initiative aimed at raising local revenue to maintain and redevelop the local facilities has not been implemented.Property rating is one of the most stable source of local revenue, which if harnessing can finance the provision and maintenance of community infrastructures.This study has examined the existing condition of neighbourhood facilities and evaluated the most significant factors that militated against the implementation of property rating.The study has collected quantitative data, and used SPSS for reliability and exploratory factor analysis; and applied Structural Equation Modelling (SEM) with Analysis of Moment Structure (AMOS) for the analysis of the measurements and the structural models.The results showed that 'Over-Reliance on Crude Oil Revenue' and 'Poor Taxation System' are the most important factors hindering the implementation of property rating.And that the 'Lack of Political Will' is a factor that remarkably influenced the condition of the neighbourhood facilities in the study area.In conclusion the study has proposed Land area-based assessment for rating valuation, using Google Earth/Map for area measurement.The proposed framework was envisaged to be cost-effective in rating valuation.It was recommended that the government should diversify revenue sources from oil-based to harness all the avenues like property rating at the municipal level.Future studies should find out, apart from 'Over Reliance on Crude Oil Revenue' and 'Poor Taxation System', whether some other factors do militate against the implementation of the property rating in the study area.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.939 | 0.931 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".