Bibliographic record
Abstract
When people tend toward a political decision, such as voting for the Republican Party, they are often attracted to this decision by one issue, such as the party’s stance on abortion, but then they come to see other issues, such as the party’s stand on taxes, as supporting their decision, even if they would not have thought so in the absence of the decision. I demonstrate this phenomenon with opinion poll data and with an experiment done on the World Wide Web using hypothetical candidates. For the hypothetical candidates, judgments about whether a candidate’s position on issue A favors the candidate or the opponent are correlated with judgments about other positions taken by the candidate (as determined from other hypothetical candidates). This effect is greater in those subjects who rarely make conflicting judgments, in which one issue favors a candidate and another favors the opponent. In a few cases, judgments even reverse, so that a position that is counted as a minus for other candidates becomes a plus for a favoredcandidate. Reversals in the direction of a candidate’s position are more likely when the candidate is otherwise favored. The experiment provides a new kind of demonstration of “beliefoverkill,” the tendency to bring all arguments into line with a favored conclusion.
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 | 0.011 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".