Bibliographic record
Abstract
What initially led me to start work on this project was the observation that the examples of fallacies and questionable argument tactics studied in textbooks of informal logic often featured examples of advertisements and political arguments of the kind that have to do with elections or with public policies. Many of them are media arguments from sources such as political speeches, commercial ads, or Internet blogs. Such arguments are especially interesting when it is evident that they were used – for example, in ads – as rhetorically effective techniques to persuade a mass audience. Formerly (and often still), such arguments tended to be classified in logic as fallacious. But more and more they are now seen as fallible (but slippery) heuristics useful to reach a tentative conclusion under conditions of uncertainty, but subject to critical questioning. The theory put forward in this book strikes a judicious balance between analyzing them as fallible but basically reasonable arguments in some cases, and criticizing them as fallacious arguments used as tactics to unfairly get the best of an opponent or deceive a mass audience in other cases. More specifically, the kinds of arguments considered throughout the book are ones often used in various communication media, including written texts, television, and the Internet, to attempt to persuade an audience to do something or accept something as true.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.439 | 0.276 |
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".