The Achimota Transport Terminal in Accra: A Model Urban Regeneration Project in Ghana?
Bibliographic record
Abstract
Over the years, government has spent millions of taxpayer’s monies to undertake urban regeneration projects (URPs) with the aim of combating the challenges of urban decay in Ghana. Several studies have argued that a number of these URPs have been left to deteriorate because there was no proper plan to maintain them. Amidst these challenges, the Achimota Transport Terminal (ATT) has been tagged as a ‘model URP in Ghana’. This paper finds out the reasons for the tag put on ATT. We adopted purposive, convenience and stratified sampling techniques to select the respondents for this study. We found two reasons for the tag on ATT – one being that the managers of ATT strictly adhere to routine and preventive maintenance practices. However, corrective maintenance was deferred. The other reason is that the terminal meets the physical (adequate parking space, availability of waiting sheds), social (creation of employment, reduction in theft cases and available cars to all destinations in Accra and beyond) and environmental (improved sanitation) dimensions of urban regeneration. Nevertheless, same cannot be said about the economic dimension (low daily sales, high maintenance cost). About 90 percent of the drivers complained vehemently of low daily sales at the terminal. We believe that delaying corrective maintenance when needed may not only mean additional cost when repairs are finally done but has the likelihood of plunging the terminal into a poor state within a short period. Additionally, since the terminal in question is serving as a model for future terminals, planning and designing of such future terminals should aim at meeting all the dimensions of urban regeneration to enhance its usage and sustainability.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".