Bibliographic record
Abstract
There are four points that must be made early about South Asian English poetry. First, the category of South Asia, while convenient, may do dis-service to those nations, Pakistan and Bangladesh, whose very raison d’être is their separateness from India and each other. The second is that these three countries do share a common political, literary and cultural history with India (as do Sri Lanka, Nepal and Bhutan) and therefore need to be distinguished only after 1947; hence, this chapter will look at pre-independence subcontinental poetry as a separate category, while looking at post-independence poetry in terms of the nations. The third is that Indian English poetry dominates the others in South Asia, having a much larger corpus. The fourth is that though we are dealing with South Asian English poetry, no such English really exists or is used by the poets except for comic purposes. Pre-independence subcontinental poetry The subcontinent saw the flowering of Indian English poetry very early (travel memoirs came even earlier) – in the 1820s. To give a sense of perspective, the British Empire was thirty-odd years away, as were the first universities in India; Macaulay’s famous ‘Minute on Indian Education’ would be written in 1835 and implemented even later. But the East India Company was already in India, and the people of Kolkata (known as Calcutta till recently), which was a cauldron of languages, races, and commercial and cultural possibilities, had already decided that English would be the language of power and emancipation.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".