World literature and migration literature
Bibliographic record
Abstract
During the past decades, literature by migrant authors has emerged in national literary fields all over the globe. This happened at different times and with diverse effects, depending on the respective socio-political contexts these authors tried to establish themselves in. Often, these authors first occupied a niche in the literary field but in many circumstances their writing and reception, together with other cultural developments, eventually had a near revolutionary effect on the literatures they worked in. In many cases, those were opened up and became more inclusive with regard to writers’ origins, ethnicities and languages. New infrastructure (such as dedicated publishing houses, literary journals and prizes) emerged and the national aspect of literary fields was questioned. In literary studies, these processes together with the aesthetic innovation brought about by migration literature, led to labelling this literature as transnational and reading it as a new world literature. \nIn the proposed article, I will first define “migration literature”, present various terms that are used in different languages, such as Migrationsliteratur, littérature migrante, letteratura migrante etc. and discuss their differing meanings. I will then present approaches of reading migration literature as world literature, first from the point of view of scholars studying migrant writers and, second, from a world literature perspective. Finally, I will shed light on the impact of an understanding of migration literature as world literature on national literary fields.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".