Voiced illustrations: The use of constructed voices in the study of argument
Bibliographic record
Abstract
A defining characteristic of discourse studies as a field is its grounding in attested data and rejection of introspective data. Researcher intuition or speculation about what discourse is like does not constitute evidence. However, the use of invented examples is not uncommon in the study of argument, a phenomenon that has received little attention. Rather than dismiss them on epistemological grounds, this paper views invented examples as a feature of the written discourse of researchers and investigates the purpose they serve as ‘voiced illustrations’. Based on an analysis of 578 voiced illustrations in 26 published argumentation research articles, the study shows that constructed voices – fictional or hypothetical voices invented by a writer or speaker – are common and explains how they are used to illustrate abstractions. This use of constructed voices in research papers bears intertextual traces of the textbook genre, a form of generic intertextuality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".