Chapter 5 Strategic Politicians, Emotional Citizens, and the Rhetoric of Prediction
Bibliographic record
Abstract
Abstract This chapter argues that strategically interacting politicians who seek to sway public opinion, routinely use predictions about the outcomes of different policies to their advantage. This produces a political environment in which ordinary citizens hear opposing and often contradictory predictions about the future consequences of a policy. The chapter reviews two examples: the Lincoln–Douglas debates and the 1997 Devolution Referendum in Scotland. Over time, serious negative predictions about consequences came to dominate both debates. Those who identify strongly with one or another party or faction can overcome the difficulty created by such user-unfriendly political environments by simply adopting their parties' positions. However, initially-unaligned message recipients, who do not begin on one side or the other, and who want to make the best and most objective choices they can, face a very difficult challenge. The chapter concludes by offering speculations about how unaligned citizens might reach their judgments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".