PROTOCOL: Evidence and gap map: studies of the effectiveness of transport sector interventions in low‐ and middle‐income countries
Bibliographic record
Abstract
Context: In the Footsteps of Mr. Kurtz Michela Wrong describes walking down the overgrown disused railway which years before had been part of a network linking DRC's copper mines to ports in Angola and South Africa.Despite new investments in the last decade-the Benguela Railway link from DRC to Angola reopened in 2018 after being closed for 34 years 2 -Africa's rail system is small compared to that in other parts of the world, and a substantial part of what there is not used (Bullock, 2009).The poor state of railway transport in Africa-and the unrealised potential of inland waterways-puts excess pressure on the fragile road transport system, so that transport costs-which are increased by uncompetitive practices-are a break on African development.While much of Africa is an extreme case, inadequate transport infrastructure is an issue across much of the developing world.There are great disparities in the quantity and quality of infrastructure.European countries such as Denmark, Germany, Switzerland, and the UK have close to 200 km of road per 100 km 2 , and the Netherlands over 300 km per 100 km 2 .By contrast, Kenya and Indonesia have <30, Laos and Morocco <20, Tanzania and Bolivia <10, and Mauritania only 1 km per 100 km 2 .3 As these figures show, there is a significant backlog of transport infrastructure investment in both rural and urban areas, especially in sub-Saharan Africa (Foster & Bricenõ-Garmendia, 2010).The situation is often exacerbated by weak governance and an inadequate regulatory framework with poor enforcement which lead to high costs and defective construction.The wellbeing of many poor people is constrained by lack of transport, which is called "transport poverty."Lucas et al. (2016) suggest that up to 90% of the world's population are transport poor when defined as meeting at least one of the following criteria: (1) lack of available suitable transport, (2) lack of transport to necessary destinations, (3) cost of necessary transport puts household below income poverty line, (4) excessive travel time, or (5) travel conditions unsafe or unhealthy.Benefits of better transport: better transport policies, infrastructure and services are widely believed to be important to boost sustainable, -----------------
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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 | 0.052 | 0.161 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.015 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.118 | 0.015 |
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".