Your body is our black box: Narrating nations in second-person fiction by Edna O’Brien and Jennifer Egan
Bibliographic record
Abstract
Abstract For a century, the disorienting effects of second-person narration have seemed peculiarly well suited to representing the experiential confusions and political contradictions of inhabiting a female body in times of national crisis. This essay examines such effects in Edna O’Brien’s A pagan place and Jennifer Egan’s “Black box,” very different narratives that similarly exploit the deictic and ontological uncertainties of second-person address. Second person in O’Brien’s novel participates in its depiction of a sexually naïve rural Irish girl confronting the conflicting pressures of enforced chastity and reproductive futurism in the name of the Irish State. Emphasis is placed on the narrative’s unusual use of past-tense second-person narration and its intriguing overlap with O’Brien’s nonfictional writings. In Egan’s story, the protean and multivocal second person suggests a sinister fusion of individual and governmental agency, effected through the protagonist’s cybernetically-enhanced body. The result is a deceptively simple critique of post-9/11 American foreign policy as an extension of paternalism and patriarchy in the domestic sphere. The patterns investigated in this paper shed light on other recent uses of the second person in other experimental narratives concerned with identity, self-formation among disenfranchised individuals, and resistance to political and cultural oppression.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".