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Record W2969765416 · doi:10.1080/08865655.2019.1653786

Gaining a Ghetto: The Resettlement of Partition-affected Bengalis in New Delhi’s Chittaranjan Park

2019· article· en· W2969765416 on OpenAlexvenueno aff
Anubhav Roy

Bibliographic record

VenueJournal of Borderlands Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsnot available
Fundersnot available
KeywordsPartition (number theory)CarvingPoliticsRefugeeWest bengalBengaliState (computer science)SociologyPolitical scienceGeographyGender studiesEconomic growthSocioeconomicsLawArchaeologyEconomics

Abstract

fetched live from OpenAlex

The Bengali sufferers of the tragic partition of India in 1947 have arguably failed to garner the political, policy, and discursive attention received by their West Pakistani or Punjabi counterparts. A case in point, Chittaranjan Park – a sub-urban neighborhood or colony of New Delhi granted as a ghetto to the Bengalis rendered rootless by the formation of East Pakistan – is rarely a muse for forays in partition studies or borderscaping. This paper, as an attempt to fill this void, traces the civil society-led lobbying movement for the carving out of Chittaranjan Park at the heart of India’s national capital, by largely relying on archived editions of the colony’s first newsletter. The narrative is linked to its contextual undercurrents of identity consciousness, state rehabilitation policy, civil-state relations, and local politics and economics by historical-evaluation. First, after highlighting how the Bengal chapter of the partition is often overlooked, this paper highlights the benefits that the then expanding city of Delhi offered its refugees in India. Second, it contrasts the Indian state’s policy response to the partition’s refugees from West Pakistan to those from the east. Third, it unpacks the idea of, and lobbying bid for, Chittaranjan Park, and examines if the colony qualifies as an ethnically-exclusive bordered space within a city.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.290
Teacher spread0.244 · 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 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
Published2019
Admission routes1
Has abstractyes

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