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Record W4302292806 · doi:10.1075/jaic.21009.pim

Argument Continuities in theory and practice

2022· article· en· W4302292806 on OpenAlexaffabout
Oxana Pimenova

Bibliographic record

VenueJournal of Argumentation in Context · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsArgumentation theoryArgumentativeArgument (complex analysis)Context (archaeology)EpistemologyAdversarial systemIndigenousPower (physics)Set (abstract data type)SociologyComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Argumentation on some public policy issues is conjugated with disagreement and power differentials. Institutionally dominant arguers control the argumentation context through imposing authority rules which sometimes incentivize them to respond to opposing arguers in a fallacious way 1 – with “the repeating tokens of the same counterarguments” and without considering the merits of opposing arguments. As produced in accordance with authority rules, such fallacies are embedded in the dominant argumentative discourse and easily pass unnoticed. To detect them, I introduce Argument Continuity (AC) – a new category of argumentative discourse analysis. AC is a set of the same arguments and counterarguments repeatedly produced/reproduced by the dominant arguer through an adversarial reasoning process to disconfirm opposing arguments and dismiss them. ACs are distinguished from other fallacies by their continuous nature and recursive way of production. ACs have their own life cycle – a chain of reasoning dynamics developing in a path-dependent fashion and increasing the cost of adopting a certain argument over time. I test the life cycle of ACs in a single case study – in consultations held by the Crown with Indigenous peoples of Canada over a controversial resource development project. Although ACs are not specific to the Crown-Indigenous relationships, they reveal how dominant arguers treat disagreement from epistemically diverse arguers. Based on observed evidence, I develop three theoretical propositions of ACs, which can serve as guidelines for researching the disconfirming mode of reasoning in other contexts of communication permeated by beliefs clash and power asymmetries.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.377
Teacher spread0.350 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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