Bibliographic record
Abstract
Past research finds that the more people perceive polarization in others the more extreme their own attitudes become. However, we do not know what affect the perception of polarization has on political neutrals and moderates. In the proposed study, we will examine how polarization perception differentially affects those with neutral, moderate, and extreme views. Specifically, we will examine how these perceptions affect political engagement and voting intention. We expect to find that neutrals and moderates’ engagement is attenuated by perception of polarization and confirm previous finding that extremists become further engaged in perceiving a polarized electorate. In this correlational and longitudinal study, we will gather voting intention measures prior to the Fall 2019 election and follow-up with respondents to determine if intention translated to action. We expect that extremists will show stronger voting intentions the more they perceive polarization whereas moderates and neutrals will show less commitment to vote the more they perceive the political system is polarized. Faculty Mentor: Craig Blatz Department: Psychology (Honours)
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".