The Relationship Between Openness to Experience and Willingness to Engage in Online Political Participation Is Influenced by News Consumption
Bibliographic record
Abstract
Openness to experience is known to be an independent predictor of online political behavior, although the degree to which this relationship is influenced by other factors has not been tested. One objective of this study was to test whether the relationship between openness to experience and the propensity to engage in online political participation is mediated by internal political efficacy and hours spent consuming news. The second objective was to determine if a preference for different news sources would be related to a willingness to participate in online political behavior. University students ( n = 419) were assessed on willingness to engage in online political participation, hours dedicated to news consumption, preference for different news sources, and internal political efficacy. Our results showed that openness to experience was related to a willingness to engage in online participation, and this was mediated by hours spent consuming news and internal political efficacy (95% confidence interval [CI] = [.0048, .32]). A preference for both semipublic and private news sources was related to greater internal efficacy (95% CI = [.2347, 1.4799]), which was in turn related to a greater propensity to engage in online political participation. These findings highlight the potential importance of news consumption for a propensity toward online political engagement.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".