Active vs. Passive Ambivalent Voters: Implications for Interactive Political Communication and Participation
Bibliographic record
Abstract
Voters express different attitudes toward competing political parties and the issues they support. In this study, a polytomous latent class analysis of their opinions regarding party-divided issues identifies several types of voters and highlights the distinction between active and passive ambivalent voters. Such a distinction is necessary to clarify the relationship between party ambivalence and political participation. Drawing on research into ambivalent attitudes, the current study postulates that active ambivalent citizens adopt amplification strategies, whereas passive ambivalent citizens adopt avoidance strategies. A comparison between them further indicates that active ambivalent citizens are motivated to fulfill their civic duties and be accountable when they seek political information, and they express more political interest than their passive counterparts. A three-wave panel survey confirms the influence of ambivalent voter types (wave 1) on political participation (wave 3), according to voters’ political orientation (i.e., civic duty motives to seek political information and interest in politics) and their interactive political communication (interactive engagement with digital political information and interpersonal political discussions) (wave 2).
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".