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
Objective Do voters hold accurate perceptions about economic conditions and what factors drive those perceptions? Some work suggests that voters are too hopelessly biased by partisanship or other commitments to be able to develop accurate perceptions of the economy upon which to base judgments of incumbent performance (Evans and Andersen, 2006). By contrast, other work shows that voters do a good job of developing accurate perceptions about economic conditions in which partisan bias is a minor influence (Lewis‐Beck et al., 2013). Methods The research note draws on a pooled data set of Canadian Election Studies from nine national elections for the period 1988–2015 to explore the relative influence of both approaches using multilevel modeling. Results Findings indicate evidence for both camps: partisan bias does exert some independent influence on shaping national economic evaluations and national economic evaluations reflect actual real‐world economic conditions. Conclusions Implications of these results suggest that economic perceptions have mixed origins that lend some, not insignificant, support to the claim that economic voting remains a viable scholarly enterprise.
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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