Bibliographic record
Abstract
Abstract The Liberalism Trap identifies a methodological problem in contemporary political theory: focus on liberalism has become an interpretive custom directing engagements with politics. Though scholars have long analyzed the meanings, merits, successes or failings of liberalism, little attention is paid to how such preoccupations shape the way we study political questions and texts. The book evaluates the effects of these preoccupations by turning to John Stuart Mill—the so-called father of modern liberalism—arguing that Mill’s canonical status as a liberal is habitually substituted for his political arguments. The book works against that substitution through a comparative reading of Mill’s proposals concerning gender, class, and empire. That comparison recovers a thinker motivated not by ideological certainties but by a politics of uncertainty and draws into view the complex strategies that Mill employs across his work on domestic and imperial questions. Recovering Mill’s uncertain politics also sets into relief the interpretive costs of reading through liberalism. That even the paradigmatic liberal is unduly constrained by this label suggests that taking a break from our preoccupations with liberalism might be warranted. In all, The Liberalism Trap integrates an innovative reading of a canonical thinker with a methodological critique of interpretive practices in contemporary political theory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".