Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Almost an entire generation of election survey data was pooled together from the United States and Canada to assess the systemic effects of televised debates. Four questions were posed: (1) Is there a general tendency for evaluations of candidates to improve or deteriorate after a debate? (2) Do evaluations of one candidate negatively correlate with changes in evaluations of opponents? (3) Do debates disadvantage incumbents? (4) Do debates advantage less popular candidates? Using “feeling thermometer” items to measure voter evaluations, four patterns are revealed. First, candidates generally gain points.The supposed mudslinging that characterizes a debate appears not to feed into any notion of cynicism. Instead, voters appear to gain an appreciation for the debaters. Second, a candidate's gain is not earned at the expense of those deemed to have “lost” the match. Third, a debate does not disadvantage an incumbent. A candidate with a record to defend stands about as much chance of benefiting from a debate as a challenger.And fourth, any evaluation gaps before a debate become narrower following a debate. This final effect, which is particularly true of American presidential debates, may reflect a debate's ability to raise awareness of less popular candidates.
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.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it