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Rhetoric Culture Theory

2022· reference-entry· en· W4309450764 on OpenAlexaff
Robert Hariman, Shauna LaTosky, Michał Mokrzan, Ivo Strecker

Bibliographic record

VenueOxford Research Encyclopedia of Anthropology · 2022
Typereference-entry
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsToronto Metropolitan UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsRhetoricPremiseConventionSociologyEthnographyEpistemologyAestheticsPolitical scienceLinguisticsSocial sciencePhilosophyAnthropology

Abstract

fetched live from OpenAlex

Abstract Pragmatic linguistics, discourse analysis, and the ethnography of speaking developed rapidly from the middle of the 20th century, when researchers began to be able to take ever smaller and more efficient audiovisual recording equipment to the field, and computers helped them play back, analyze, and discuss these especially rich new data with their interlocutors on location and with their colleagues at home. Part of this newly energized research was the comparative study of rhetoric—that is, of how distinctive speech practices could have persuasive effects. It soon led to the finding that specific forms of culture produce specific forms of rhetoric, as when economic horizons (hunters, herders, cultivators, etc.) provide specific metaphorical repertoires. However, a further finding took longer to emerge. It was first articulated by the rhetoric culture project, which seeks to explore not only how culture structures rhetoric but also how rhetoric structures culture. This fundamental chiasmus was initially discussed at several international conferences in Germany and the United States and has been elaborated in nine volumes of the Berghahn Books series Studies in Rhetoric and Culture (2009–2022). A key premise of Rhetoric Culture Theory (RCT) is that human beings are neither fully free nor fully determined in what they can do, and that this tension is mediated by the continual generation of discourses from the interaction between intention, convention, and performance. Stephen Tyler has provided a model for this complex process which illustrates the open-ended and emergent nature of discourse and explains how cultures, with their diverse customs, conventions, habits, and lifestyles, are self-organizing configurations continually recreated, negotiated, and changed through texts and performances. Cultural explanation is advanced through attention to processes of argument and appeal, dissonance and resonance, variation and feedback, and the like, but the results may not be objectively functional. Miguel de Cervantes Saavedra’s Don Quixote de la Mancha was chosen as RCT’s icon and telling example of this rhetorically produced and potentially fantastic nature of culture. RCT is also inspired and supported by understandings of the power of the word in other (and especially non-European) cultures. An example of this is Baldambe (Father of the Dark Brown Cow), an elder from Hamar, southern Ethiopia, who provided “historic” moments where in collaboration with the ethnographer spoken words were transformed into written ones, and texts with their own distinctive features and literary style emerged as documented in a number of publications. RCT is also influenced by the tenor of its time, not least an impending climate collapse and other threats that characterize the Anthropocene. Rhetorical and cultural abundance can be part of the existential crisis and resources for renewal on behalf of equity and sustainability. Reflecting on the relationship between speech practices and deep problems can reveal how all of culture is challenged by vicissitudes that are unanticipated and that scale up disastrously, and that call up inventive answers while testing the limits of human ingenuity.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1920.001

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.

Opus teacher head0.051
GPT teacher head0.397
Teacher spread0.346 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2022
Admission routes1
Has abstractyes

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