Terrestrial Humanism and the Weight of World Literature: Reading Esi Edugyan’s<i>Washington Black</i>
Bibliographic record
Abstract
Through an extended reading of Canadian author Esi Edugyan’s novel,Washington Black(2018), this article aims to revise and reinsert both the practice of close reading and a radically revised humanism back into recent world literature debates. I begin by demonstrating the importance of metaphors of weight to several theories of world literature, before tracking how, with the same metaphors, Edugyan challenges Enlightenment models of earth, worlds, and humanism. The article draws on the work of several theorists, including Emily Apter, Katherine McKittrick, Steven Blevins, Edward Said, and Frantz Fanon, to argue that “terrestrial humanism” might provide a framework from which to develop a grounded, politicized, earthly practice of close reading world literary texts. The aim is not to arrive at a prescriptive or “heavy” methodology, but to push instead for a reading practice that remains open to the contrapuntal geographies, affective materialisms, and radically humanist politics of literary texts themselves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".