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Record W2612694072 · doi:10.1080/19406940.2017.1313298

A window into India’s development story – the 2010 Commonwealth Games

2017· article· en· W2612694072 on OpenAlexaff
Mitu Sengupta

Bibliographic record

VenueInternational Journal of Sport Policy and Politics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsToronto Metropolitan University
FundersLeverhulme Trust
KeywordsCommonwealthState (computer science)SlumNeoliberalism (international relations)Asian gamesPolitical scienceWork (physics)Economic growthSociologyPolitical economyDevelopment economicsLawEconomicsPopulationAdvertisingEngineeringBusinessDemography

Abstract

fetched live from OpenAlex

Sports mega-events tend to follow a top-down, increasingly neoliberal, developmental logic that reflects the aspirations and material interests of their host countries’ elites. The 2010 Commonwealth Games (CWG), held in Delhi, India, falls within this pattern. It also provides valuable insight into the country’s larger development trajectory. I locate the roots of the CWG’s troubling effects, such as slum demolitions and securitisation of the city, not within neoliberalism per se, but within a paradigm of ‘development’ that equates ‘progress’ primarily with catching up with the West. I emphasise the striking similarities between the CWG’s impact on Delhi, and that of the 1982 Asian Games, which India hosted while it was still following a state-interventionist development model. Much like the organizers of the CWG, the organisers of the Asian Games tried to convert Delhi into a ‘modern’ city cleansed of shanty towns and street vendors. Although this effort was ultimately unsuccessful, it produced many adverse consequences for the city’s marginal populations. When compared, the legacies of the CWG and Asian Games indicate that it is the larger paradigm of development at work in India that ought to be questioned, not only how it is actualised through state-interventionist or free-market (neoliberal) policy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.383
Teacher spread0.344 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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