Bibliographic record
Abstract
Accra and Kumasi expanded rapidly over the last 35 years, with residential developments appearing on the periphery, often with limited investments in infrastructure or essentials services. This lowers proximity diminishing accessibility in these cities, especially in the absence of well-functioning transit system with mass-transit, bike lanes, sidewalks, and so on. In fact, the road network, itself, can make it difficult to travel via non-car modes, e.g., limited intersections worsen walkability. In cities, like Accra and Kumasi, anyone living in those areas who might rely on walking or trotros (which is most of the population) might find themselves cut off from essential daily needs, particularly women who are carrying out caregiving roles alongside their employment. Creating proximity can drastically improve the quality of life and feasibility of non-car modes. Even though, there is general acceptance that land-use and transport need to go together within government, several governance and institutional barriers stand in the way of realising this. This policy paper explores two levers to improve accessibility (proximity and road network upgrading) and explores paths for better co-ordination within existing institutional frameworks.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.308 | 0.115 |
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".