Social Security and the Third Phase
Bibliographic record
Abstract
In the last weeks of the campaign, Al Gore staged a recovery, one fueled in large part by a single issue. That issues provided the impetus is itself a remarkable fact. George W. Bush tried from the start to occupy the center in issue perceptions, especially on the New Deal/Great Society agenda traditionally owned by the Democrats. It was not obvious that a Republican candidate could capture traditionally Democratic ground, for such a strategy runs contrary to predictions from theories of “issue ownership” (Simon 2002). But the Gore campaign evidently concluded that Bush had succeeded, for Gore came to see his task as pushing Bush back to the ideological right, perceptually speaking. In particular, Gore sought to persuade voters that a key Bush proposal, a plan to reform Social Security, far from securing the program's future, profoundly threatened it. Gore succeeded, but only incompletely. What this chapter shows is the specific manifestation among issues of the tug-of-war between news and ads described in general terms in Chapter 4. The account begins with each side's initial rhetoric on the issue. At the Republican convention, Bush delineated his plan for Social Security in some detail; at the Democratic convention, Gore voiced his opposition. But for some time thereafter, neither side devoted much attention to the issue, at least in advertising. At the first debate, that changed as Gore launched an energetic attack on the Bush proposal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".