Narrativised Historicity in Arif Anwar’s <i>The Storm</i>
Bibliographic record
Abstract
With five different plotlines over a prolonged course of sixty years in British Bengal and East Pakistan, Canadian-Bangladeshi writer Arif Anwar’s debut novel The Storm (2018) captures historical ethos through a series of micronarratives. An occupied Burma during WWII, a 1965 pre-Partition Calcutta, and a devastated Bhola after the 1970 cyclone — all these feature in this historiographic metafiction. Each character contributes as an independent narrator for the greater geopolitical mise-en-scènes of their times, rediscovering a forgotten past. This paper aims to identify the narrativised version of historicity that Anwar considers “authentic” in his novel. The findings propose a reciprocal commitment between narration and history on the basis of lived experiences or memories, phenomenological recurrences, and intersubjective surroundings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".