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Record W2492672834 · doi:10.22158/wjssr.v3n3p352

Public Transportation in South Africa: Challenges and Opportunities

2016· article· en· W2492672834 on OpenAlexaff
David P. Thomas

Bibliographic record

VenueWorld Journal of Social Science Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsMount Allison University
Fundersnot available
KeywordsPublic transportGovernment (linguistics)PovertyPublic policyState (computer science)Public administrationInequalityEconomic growthPolitical scienceBus rapid transitTransportation infrastructureBusinessEconomicsEngineeringTransport engineeringLaw

Abstract

fetched live from OpenAlex

<p><em>This article engages with several important questions regarding the state of public transportation in South Africa. It provides a brief description of the historical legacy of apartheid in relation to public transport, and the challenges this posed to the government after 1994. This is followed by a summary of the changing policy frameworks in the post-apartheid era, and an examination of the current policies, trajectories, and major transportation projects within the country. For example, this includes a more detailed discussion of major infrastructure projects such as the Gautrain and Bus Rapid Transit (BRT) in the form of Rea Vaya. Overall, the article argues that the South African government is struggling to build an inclusive public transportation infrastructure that addresses issues of poverty, access, and inequality. Finally, the article will conclude with a set of recommendations to build a more inclusive transportation policy framework for South Africa. </em></p>

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.006
Scholarly communication0.0070.009
Open science0.0010.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.381
GPT teacher head0.428
Teacher spread0.048 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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