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Record W2917534599 · doi:10.13008/2151-2957.1282

Scientific Futures for a Rhetoric of Science: "We do this and they do that?" A Junior-Senior Scholar Session, RSA 2018, Minneapolis, Minnesota, USA; 1 June 2018

2019· article· en· W2917534599 on OpenAlexaff
David Gruber, Randy Allen Harris

Bibliographic record

VenuePoroi · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRhetoricRhetorical questionPerformative utteranceSociologyMateriality (auditing)Science studiesFutures contractEpistemologySocial scienceAestheticsLiteraturePhilosophyLinguisticsArt

Abstract

fetched live from OpenAlex

Growing attention to a rift between epistemology and ontology, between words and things, sets new challenges and invigorations for a Rhetoric of Science that traditionally aims to “analyze and evaluate the persuasive communications of scientists” (Ceccarelli, 2017, para 6). Rhetoricians confront a vibrant, new intellectual space where scholars across disciplines are seeking to better account for bodies and moving to “include the materiality of our ambient environs” in their analyses (Rickert, 2013, p. x). The question, in light of material expansions, is what is a Rhetoric of Science, and what are its futures? In response to the Rhetoric Society of America’s 2018 conference call for junior and senior scholars to discuss “major developments in rhetorical studies,” we offer a Feyerabendian innovation-meets-dogma performative session: the junior scholar, representing innovation, argues that Rhetoric of Science must move aggressively beyond a study of texts and scientific language to account for continuous technological, social, and biological entanglements; specifically, to expand the field’s practices to include neuro-cognitive approaches and other forms of experiment. The senior scholar, representing dogma, expresses caution, arguing that the domain of a Rhetoric of Science is still symbols and semiosis; specifically, that looking at “ambient rhetorics” and “entanglements” is another approach, not a foundational shift.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.012
Scholarly communication0.0140.010
Open science0.0010.007
Research integrity0.0060.014
Insufficient payload (model declined to judge)0.0190.005

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 head0.043
GPT teacher head0.280
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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