Media Platforms, Formats, and News Cultures
Bibliographic record
Abstract
ge and gender present the face of journalism, while years of professional experience indicate journalists' commitment to their work.Since the previous wave of the Worlds of Journalism Study (WJS2), conditions for journalism and journalists have undergone significant shifts.Changes in information channels, alongside political and economic headwinds, have left visible marks on journalists' age and experience profiles, while other demographic characteristics, such as gender and education level, have remained relatively stable. GENDERGlobally, journalism remains slightly male-dominated, with just under half of respondents across the 75 countries identifying as female (see Table 2).This represents only a modest increase in female participation compared to WJS2 (Josephi et al., 2019) and earlier studies, which consistently identified journalism as predominantly male (Weaver et al., 2012).Female representation varies considerably across countries, reflecting diverse national contexts and levels of gender empowerment.Women constitute a majority of journalists in 30 of the 75 countries surveyed, exceeding 60% in 11 countries.Most of these are in Northern and Eastern Europe, including Moldova-where nearly three in four journalists are female-as well as Ukraine, Latvia, Romania, Croatia, Lithuania, Bulgaria, and Finland.Southeast Asian countries such as Singapore and Thailand also report high female representation.Conversely, significant gender disparities persist in favor of men in 14 countries, particularly in South, Central, and East Asia, including India, Pakistan, Uzbekistan, South Korea, and Indonesia.Women are also clearly outnumbered in several Arab countries, such as Yemen, Egypt, and the United Arab Emirates (UAE).In Latin America, women generally remain a minority, except in Brazil, Venezuela, and Cuba.Worldwide, 135 journalists (0.4%) identified as "other."North America (Cuba: 2.9%; USA: 2.7%; Canada: 1.9%) and Thailand (2.4%) reported the highest percentages for nonbinary genders.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".