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Record W2918009664 · doi:10.4324/9781315712482-31

The politics of urban management and planning in African cities

2018· book-chapter· en· W2918009664 on OpenAlexaboutno aff
Andrea Rigon, Joseph Macarthy, Braima Koroma, Alexandre Apsan Frediani

Bibliographic record

VenueUCL Discovery (University College London) · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)GeographyUrban planningPopulationPoliticsMeaning (existential)Economic growthSocioeconomicsPolitical scienceEcologyArchaeologySociologyDemographyBiologyEconomics

Abstract

fetched live from OpenAlex

Half of Africa’s population is expected to live in a city by 2035, up from 40 per cent today. This is a testament to the fact that a quarter of the world’s fastest-growing cities are in Africa and 52 African cities already have more than 1 million inhabitants each. But these cities are only projected to absorb a quarter of the growth in urban populations, meaning that small and medium cities will host the majority of new urban dwellers (UN-Habitat, 2014: 23–25). African cities are the most unequal in the world, posing a major challenge to their future (UN-Habitat, 2010: 2).

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.016
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.212
Teacher spread0.193 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations11
Published2018
Admission routes1
Has abstractyes

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