The societal context of professional practice: Examining the impact of politics and economics on journalistic role performance across 37 countries
Bibliographic record
Abstract
The impact of socio-political variables on journalism is an ongoing concern of comparative research on media systems and professional cultures. However, they have rarely been studied systematically across diverse cases, particularly outside Western democracies, and existing studies that compare western and non-western contexts have mainly focused on journalistic role conceptions rather than actual journalistic practice. Using journalistic role performance as a theoretical and methodological framework, this paper overcomes these shortcomings through a content analysis of 148,474 news stories from 365 print, online, TV, and radio outlets in 37 countries. We consider two fundamental system-level variables—liberal democracy and market orientation—testing a series of hypotheses concerning their influence on the interventionist, watchdog, loyal-facilitator, service, infotainment, and civic roles in the news globally. Findings confirm the widely asserted hypothesis that liberal democracy is associated with the performance of public-service oriented roles. Claims that market orientation reinforces critical and civic-oriented journalism show more mixed results and give some support to the argument that there are forms of “market authoritarianism” associated with loyalist journalism. The findings also show that the interventionist and infotainment roles are not significantly associated with the standard measures of political and economic structure, suggesting the need for more research on their varying forms across societies and the kinds of system-level factors that might explain them.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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".