Calling People Names: Reading Imposture, Confession, and Testimony in and after Michael Ondaatje's The English Patient
Bibliographic record
Abstract
Lacan states that although naming is ultimately an arbitrary marker of identity, it nevertheless functions as a stabilizing guarantee that we can agree upon identity in some way. In The English Patient, Michael Ondaatje's deferral of names in the first section, and the English patient's withholding of his identity — whether through genuine trauma or through imposture — forces readers into a partnership with the characters to adopt an alternative narrative practice in which we might forego the desire for stable identities. The related sub-theme of erasure, of nations, of history, of individual identity, is attempted through a progression of confession through to testimony, where transformative renewal might be forged. The desire, both of the characters and of the reader, for a fully named world, needs a re-evaluation.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.022 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| 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".