Tyranny of Balance in News Increases Climate Change Denialism in Indonesian Society
Bibliographic record
Abstract
Climate change denialism, the rejection of overwhelming scientific evidence about the negative impacts of human activities on the environment, is a significant hurdle in mitigating climate change.This study investigates the influence of communication factors on climate change denialism among 124 students in Cilegon, Banten.Factors examined include news immediacy, scientific communication competence, message tone, tyranny of balance, and message narrative.Multiple regression analysis revealed only the tyranny of balance in news reporting significantly impacted climate change denialism (p < 0.001).Other variables, including belief in conspiracy theory, news immediacy, science communication competence, message tone, and message narrative, had no significant effect.These findings underscore the crucial role of media bias in climate change denialism, particularly in the context of emerging, tropical, and island nations.Future research should scrutinize journalistic principles and mass communication about climate change denialism.However, the methodology has limitations, including a homogeneous student sample and potential recall bias, necessitating more diverse sampling and experimental methods in future studies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".