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Record W4280583986 · doi:10.3138/cras-2021-011

Modern Liberty, Partisanship, and Identity: On Mark Lilla and Yuval Levin

2022· article· en· W4280583986 on OpenAlexvenueno aff
Trevor Shelley

Bibliographic record

VenueCanadian Review of American Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Theory and Influence
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsIndividualismIdentity (music)ModerationSociologyEnvironmental ethicsAestheticsEpistemologyPolitical scienceLawPhilosophySocial psychologyPsychology

Abstract

fetched live from OpenAlex

Applying Pierre Manent’s description of the modern regime as an “organization of separations,” this essay retraces the respective developmental accounts of this regime in the early works of Yuval Levin and Mark Lilla—who each analyze a particular separation—and demonstrates its influence on their more recent American political commentary. We gain a deeper understanding of key differences in conservative and liberal approaches to revitalizing contemporary civic life and renewed appreciation for the inescapability of partisanship in thinking and acting politically. Both Levin and Lilla—scholars and public intellectuals alike—emphasize the importance of reason in undergirding free and deliberative politics, but they differ on how it emerges and is best upheld, which in turn influences their proposed remedies for today’s growing individualism, meeting the challenge of identity politics, and restoring moderation to the present political situation. This essay thereby demonstrates that foundational thinking shapes analyses and prognoses of present political discontents, as much as contemporary concerns about partisanship and identity are endogenous to modern liberty.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.374
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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