ANALYSING THE PATTERN AND URBAN PLANNING IMPLICATIONS OF SPRAWL ON QUALITY OF LIFE IN KADUNA METROPOLIS - NIGERIA
Bibliographic record
Abstract
Various studies have highlighted different urban problems that have affected the quality of life of the urban dwellers in both developed and developing nations due to rapid socio - economic changes and increase in population. The aim of this study is to assess the perceived indicators of quality of life, so as to evolve strategy of upgrading the decayed urban communities. The multistage sampling method was adopted as a sampling technique. The following settlement were selected for the study, Down Quarters, Kurmin Gwari and Badarawa-Kwaru because of its slum like settlement. A total of 406 household’s heads/members were served with questionnaires, while 380 were returned and properly filled. Twelve (12) key informants were also interviewed in all the selected communities. The results show that more than three quarter of respondents get their water from hand dug well (M= 4.2151), and a little less than one quarter admitted to get water from Pipe borne, boreholes (M= 1.4272) and other sources. The study concluded that due to high population concentration of people, government could not provide the necessary amenities and services required by the teeming population therefore, the available ones were over stretched and became dilapidated and decay set in. The study recommends that the government of Kaduna state should embark on urban renewal which will prevent decay, clear areas bad areas, upgrade building, facilities and expand metropolis roads.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".