Comparative Postcolonialisms: Storytelling and Community in Sholem Aleichem and Chinua Achebe
Bibliographic record
Abstract
This paper compares Sholem Aleichem’sTevye the Dairymanand Chinua Achebe’sArrow of God. Despite all their obvious differences in terms of cultural traditions and historical moments, the two authors’ fundamental commitment to modes of storytelling allows us to draw parallels and counterpoints between them. In both works storytelling is shaped by the essential polysemy of orality (such as the collocation of proverbs, gnomic statements, and anecdotes as crucial aspects of the stories being told), as well as an orientation toward ritual (in terms of the formal repetition of storytelling motifs and devices). In theTevyestories, the first-person narration is addressed to various explicit and implied addressees and gives the impression of an immediate orality, whereas inArrow of Godthe third-person narrator is coextensive with the one we encounter inThings Fall Apartin its quasi-ethnographic orientation. In both texts, storytelling and orality are mediums for identifying with an imagined community.Imaginedimplies a nonideal relationship to existing communities, something that is made clear in the agonistic infrastructure of the two central characters’ minds. The paper argues for seeing this agonistic infrastructure as a form of “contexture,” that is to say, a way to provide texture to the historical contexts in which they were written and to which their referential relays point us to.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".