Active vs. Passive Ambivalent Voters: Implications for Interactive Political Communication and Participation
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
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).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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