Body as Battleground: Acts of Ingestion in D’Aguiar’s Feeding the Ghosts and Philip’s Zong!
Bibliographic record
Abstract
This article explores the trope of ingestion in two literary representations of the 1781 Zong massacre, Fred D’Aguiar’s novel Feeding the Ghosts (1997) and M. NourbeSe Philip’s book-length poem Zong! (2008). In drawing attention to acts of ingestion, I seek to expand scholarly discussion of these two texts, which has to date somewhat stalled around framing them as confrontations with the traumatic past. Grounded in scholarship that argues that ingestion forms “political subjects” by “fusing the social with the biological” (Tompkins 1), this article posits the act of ingestion as a navigation of socio-historical/political forces. In these two texts, ingestion functions within systems of power wherein those in privileged positions can be consumers while the enslaved are excreters, with bodies in states of dissolution. Those benefiting from the slave trade are critiqued in these texts for their dystopic overconsumption and cannibalistic voraciousness, while those enslaved are portrayed in terms of the impossibility of achieving nourishment even when able to eat or drink. Although common theoretical assumptions link ingestion with empowerment, my exploration of these texts reveals the insufficiency of such an assumption. Since to consume is also to confront one’s dependence on and vulnerability to outside matter, for the enslaved the act of ingestion can be an incorporation of—and hence surrender to—one’s enslavement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".