Mapping Minor/Small and World Literatures
Bibliographic record
Abstract
Mapping Minor/Small and World Literatures: Periphery and Center makes a declarative intervention in debates about world literature, redefining the boundaries between the center and periphery to rejuvenate long-established assumptions about significance and insignificance. In this book, African American literature (emerging from the often overlooked pink periphery, a cramped space of minor literature), works from the Faroe Islands, Basque literature, First Nation Canadian literature, Western narratives about peripheral China, Kurdish literature, the ultraminor literary space of Antigua, the 'favela' of Brazilian literature, as well as the hyperlocal narratives of Australian and New Zealand literature are all studied for their meaningful role within the world literary system. Additionally, working-class writing and the literary contributions of individuals on the margins of their own societies are given a voice, ensuring that the world literary space does not merely represent the perspectives of dominant elites. Unlike other descriptions of world literature, which have frequently allowed the grandeur and breadth of the global to overshadow the imperative for authentic literary biodiversity, this anthology, featuring contributions from diverse scholars representing various countries and backgrounds, actively deconstructs the structures of power and domination inherent in Western-European-centered world literature, minor literature, and small literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".