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Record W4237972193 · doi:10.18192/cjcs.v0i5.2409

Reading Silence

2018· article· en· W4237972193 on OpenAlexvenueno aff
Larry L. Jackson

Bibliographic record

VenueConversations The Journal of Cavellian Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy, Ethics, and Existentialism
Canadian institutionsnot available
FundersUniversity of CambridgeHarvard University
KeywordsSkepticismEpistemologyPolitical philosophyPoliticsAppealMeaning (existential)Reading (process)PhilosophyEconomic JusticeSilenceDemocracySOCRATESSociologyAestheticsLinguisticsLaw

Abstract

fetched live from OpenAlex

Stanley Cavell roams across a wide range of fields in his first book, Must We Mean What We Say? most obviously those of epistemology, ethics, and aesthetics. But nowhere in the book’s ten essays does he advance an explicit political theory. Still, this book, published in 1969 and written over the course of the preceding decade, quietly poses persistent political questions, even in essays on such topics as skepticism and King Lear, Kierkegaard’s Book on Adler and Beckett’s Endgame, atonal music and ordinary language philosophy. Just who is the “we” spoken of in the book’s title (we philosophers? we Americans? we human beings?)? Is there any relationship between democratic equality and the philosophical appeal to our everyday language, as described in the book’s eponymous essay? 1 Does the account that Cavell offers in his piece on Wittgenstein of practices and behaviors shared across cultures—the “whirl of organism” of our forms of life—suggest a nascent theory of human solidarity? Our freedom in language and the responsibility we bear for meaning, topics of the book’s opening essays, raise the question of what we might owe to one another and how we might offer—or withhold—it in our choices of words. Is this the beginning of a theory of justice? The concept of acknowledgment, described in the book’s final essays as a response to the challenge of skepticism, shifts the problem from what I can know to what I might do. Is this a theory of moral or political action (or both)?

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.110
GPT teacher head0.316
Teacher spread0.205 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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