Predicting Trump and Presenting Canada in Philip Roth's <i>The Plot against America</i>
Bibliographic record
Abstract
In an essay published in the New York Times a month prior to the release of The Plot against America, Philip Roth writes that it “would be a mistake” to “take this book as a roman à clef to the present moment in America” (“Story”). Despite this, many reviewers and critics find it nearly impossible to ignore the (albeit inexact) parallels between the historical-political moment Roth reimagines and twenty-first-century American politics. The election of Donald Trump demonstrates the ease with which Roth's fictional political moment has manifested itself in the current state of politics in the United States. However, to concentrate exclusively on drawing these connections and to ignore Roth's historical focus is not productive. Notably, Canada in Roth's fiction is depicted as a place of liberation from anti-Semitic persecution, yet the real Canada during this era, a period that has been called “the bleakest chapter in Canadian history” (Klein xviii), very closely resembles Roth's fictional America.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".