Using and Trusting in Media, Type of Governance and the Political Trust of University Students: A Case Study
Bibliographic record
Abstract
Political trust in governments and political organs is a significant determinant of legitimacy. This quantitative study, using survey and questionnaire, has studied and investigated the effects of two variables of the state’s governing method and performance, as well as soft power on students’ political trust. The sample of the study included 400 bachelor’s and master’s students studying at Shiraz University. The result revealed a positive relationship students’ use of domestic media and trusting their contents and their political trust on the government and the political institutions. There was a negative and inverse relation when it came to the Internet and satellite channels. In terms of government performance indicators (including sense of security, notion of rule of law, services, accountability and political effectiveness) and political trust, a positive relationship was observed. However, an inverse and significant relationship was seen between the thought that there’s discrimination and corruption and political trust. The multivariate analysis showed that the variables namely Trust in news of local media, notion of corruption in institutions, notion of accountability, responsibility and supervision in the government and administration, sense of security, notion of rule of law, and sex (being man), totally explained 49.7% of changes in political trust.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".