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Record W4400129500 · doi:10.1080/23792949.2024.2364619

Connecting through public transport: accessibility to health and education in major African cities

2024· article· en· W4400129500 on OpenAlexaff
Aiga Stokenberga, Eulalie Saïsset, Tamara Kerzhner, Xavier Alegre

Bibliographic record

VenueArea Development and Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPublic transportPublic healthEconomic growthBusinessGeographyEnvironmental planningRegional scienceTransport engineeringMedicineEconomicsEngineeringNursing

Abstract

fetched live from OpenAlex

Transport matters for health and education outcomes, by ensuring physical access to crucial facilities. Using spatial modelling techniques and routable public transport service data, this study assesses the effectiveness of mostly semi fixed-route public transport systems in connecting people to advanced healthcare and education facilities in African cities. Uncovering significant pockets of ‘accessibility poverty’ – travel times above an acceptable level – it underscores the inequality in access within the cities, disproportionately affecting poor populations. Proximity of public transport to homes matters but has limited impact, due to how the routes, operated mainly by informal service providers, are allocated across the urban space and the low technical performance. The low ‘value added’ of public transport compared to walking helps explain the prevalence of foot travel. Tailored policy interventions – improving the public transport systems and, equally importantly, ensuring more equitable spatial distribution of advanced healthcare facilities – emerge as crucial strategies for addressing accessibility poverty.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.005
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.053
GPT teacher head0.364
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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