Polycentric Employment Growth and the Commuting Behaviour in Benin Metropolitan Region, Nigeria
Bibliographic record
Abstract
The paper investigates the emerging pattern of journey to work traffic that characterises the employment centres of a fast growing African city with reference to the case of Benin region, Nigeria. This is achieved by identifying and extracting the significant employment centres of the region. On the one hand, factor analysis and Getis-Ord statistic were systematically used to identify the spatial configuration of the region’s employment. Regression models on the other hand, were used to estimate the relationship that exists between job decentralisation and travel behaviour. Factor analysis and Getis-Ord statistic identified four significant employment clusters in the region. Multivariate and bivariate regression models were further used to explore the dynamics of commuting behaviour in response to decentralisation of employment centres. It is found that employment spatial structure exerts significant influence on all dimensions of commuting pattern of the region. The result shows that decentralisation of jobs in the metropolis has led to a reduction in commuting times, travel distance and significantly influence the modal choice of commuters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".