The Relevance of People's Attitudes Towards Freedom of Expression in a Changing Media Environment
Bibliographic record
Abstract
The article outlines arguments for the relevance of people's attitudes towards freedom of expression: It is a fundamental principle of democracy that if a virtue does not receive support from the population, it will not be anchored in law and its foundation is endangered in the medium term.People's support for free speech is becoming even more influential because authoritative control of internet communication is faced with difficulties.Furthermore, with the development of social media users gain new opportunities to publicly express their opinions attaching even more importance to normative self-regulation.As a matter of fact, these increased opportunities of self-regulation may either enhance or decrease the exercise of expression rights.Thus, citizen's endorsement of free expression is a valuable indicator of the status of freedom of expression in a country.To approach to the subject empirically, the paper systematizes findings on people's attitudes towards free speech: Most people believe in freedom of expression in the abstract.Willingness to apply the right to opposing groups, however, is lower.Perceived threats, confidence in democratic principles, mode of communication, and personality variables influence tolerance of expressions.Finally, a research agenda is put forward to examine appreciation of free expression, its antecedence, and implications.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".