Secular study and suffering: J.M. Coetzee's “The humanities in Africa”
Bibliographic record
Abstract
Prominent literary philosophers as diverse as Martha Nussbaum, Kwame Anthony Appiah, and Gayatri Chakravorty Spivak have envisioned a role for the humanities in fostering more ethical relationships on a global scale. Through a close reading of JM Coetzee's fifth “lesson” in Elizabeth Costello: eight lessons, this paper interrogates the limits of the humanities for promoting secular salvation. “Lesson five: the humanities in Africa”, I argue, troubles the distinction between secular teaching and religious faith as alternatives to living in a world imbued with suffering, by suggesting that neither the humanities nor religion materially offers an escape from suffering – that neither secular nor divine salvation exists beyond hope and faith. The lesson asks: do the humanities offer anything besides the promise of salvation to its students? If the secular salvation offered by the humanities fails to engage substantively with what one of Coetzee's characters calls “the reality of Africa”, despite calls for mutual understanding or an understanding of mutuality, how do we rethink the humanities? How do we imagine, and how might we re-imagine, the relationship between the humanities and quotidian suffering? Perhaps as obsessive, repetitive, imperfect performances, both the humanities and religion exist as rituals through which to live with others who are (also) suffering.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".