An Ever-Expanding World Literary Genre: Defining Magic Realism on Wikipedia
Bibliographic record
Abstract
Magic realism is a disputed genre in world literature scholarship today. While many Latin American critics have advocated for its historical and geographical significance, others see it as an inherently postcolonial aesthetic formation, a worldwide literary trend, and even a global commodity. Indeed, since its emergence in the first half of the 20th century, magic realism has remained an attractive and active category, as new artworks are classified as such worldwide. To address these tensions, this essay engages with definitions, general information, and lists of authors and literary works classified as magic realists on Wikipedia. To do so, we compile a thorough database of all writers mentioned in Wikipedia’s entries for magic realism in fifty-six different languages. We visualize this data and close-read Wikipedia entries to understand better which writers are most often identified as magic realists, to which literary and linguistic traditions they belong, and how definitions of magic realism in different languages interact. We trace how the narrow and broad definitions of magic realism tend to both compete and overlap on Wikipedia. We argue that magic realism on Wikipedia can be better understood as a glocal phenomenon. In this sense, we reflect on what the worldliness of magic realism means in a non-academic context and ask how the broad circulation of magic realism can inform our understanding of world 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".