Bibliographic record
Abstract
Appeals to fear and appeals to pity are two types of argumentation widely used in the media in political debates and advertising by advocacy groups, public relations firms, governments, and corporations. Johnson (2000, p. 269) has emphasized that mass media rhetoric, to be effective, needs to take the human emotions, in particular, fear and pity, into account. Both types of rhetorical argumentation can have a tremendous emotional impact on a mass audience, when presented in the right way. Mass media argumentation as a persuasive effort involves strategic maneuvering based on advocacy, audience adaption, and presentational devices, which are used to resolve a difference of opinion in one's own favor (van Eemeren and Houtlosser 1999b, 2000, 2001, 2002). On the other hand, both kinds of arguments are so well known to be subject to exploitation and manipulation that they have been traditionally classified in logic as fallacious. Recently, it has come to be recognized, however, that the traditional blanket condemnation is not warranted (Walton 1994). Appeals to emotion should be generally recognized as having legitimate standing as being, under the right conditions, reasonable arguments carrying some weight in shifting a burden of proof in a balance of considerations case where exact calculation of the outcome is not a practical possibility. But if appeals to fear and pity are sometimes rational arguments, how can we strike the right balance between recognizing their rhetorical power and the logical defects they admittedly have in some instances?
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".