Gaining a Ghetto: The Resettlement of Partition-affected Bengalis in New Delhi’s Chittaranjan Park
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".