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The Post-Imperial Urban Environment

2007· book-chapter· en· W3102869678 on OpenAlexaboutno aff
William Beinart, Lotte Hughes

Bibliographic record

VenueOxford University Press eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyUrbanizationIndigenousColonialismPopulationUrban agglomerationEconomic growthSocioeconomicsPolitical scienceEconomyArchaeologyDemographySociologyEcology

Abstract

fetched live from OpenAlex

In this chapter we turn to themes of race, space, environmental justice, and indigenous reassertions in the post-colonial city. We will use as examples: services and urban planning in Singapore; riots in Sydney; and a comparative discussion of parks and public symbols. Although the location of cities had largely been fixed in the colonial period, they were undergoing rapid change by the mid-twentieth century as communities from the surrounding countryside poured into the urban areas. At the beginning of the twentieth century, one tenth of the world’s population lived in cities; by its end more than half did so. In 1900 the ten largest cities were located in Europe and the US, with the exception of Tokyo at seventh. By the early twenty-first century no European urban agglomerations were in this league. The balance shifted from the West to the rest, especially after 1950. Of former colonial cities, Greater Mumbai with about 16 million people, Kolkata (13 million), and Delhi (13 million) were in this group. Mumbai had housed around one million people in 1911. Cities in non-settler states became increasingly dominated, demographically, by the descendants of rural communities from their hinterlands. While English often served as a common medium of communication, regional languages also urbanized with their speakers. Overall, urbanization was linked with rising living standards. But, especially in mega-cities, the gap increased between the rich and overwhelming numbers of urban poor, most of whom were not able to make it into formal employment. Rates of growth in former settler cities were usually less sudden, but they also became increasingly culturally diverse. Canadian cities are one example. The small migrations of indigenous people were only one reason for this. Their increasing multi-ethnicity resulted largely from new sources of global migration: for example, the movement of people from non-British parts of Europe, from the Caribbean, as well as African Americans, Indians, and East Asians. Post-colonial conflict created new diasporas: some of the 80,000 Ugandan Asians expelled by Idi Amin in 1972 went to Canada, and Toronto became home to the single largest population of expatriate Somalis.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.013
Scholarly communication0.0080.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.034
GPT teacher head0.226
Teacher spread0.192 · 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 designQualitative
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

Citations0
Published2007
Admission routes1
Has abstractyes

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