The Print Media Convergence: Overall Trends and the COVID-19 Pandemic Impact
Bibliographic record
Abstract
The study presented in the paper aims to analyze the Russian print media market before and during the COVID-19 pandemic, as well as the prospects of local media transformation in the challenging environment. In the pre-pandemic decade, there was a growing body of literature on media convergence in emerging markets confirming that this concept is growing in importance as a strategic path of conventional media transformation. Still, the research on the Russian conventional media transformation is scarce, the impact of the COVID-19 pandemic risks on Russian print media and their business models have not been investigated so far. To fill the gap, we combined desk research, processing of published industry statistics, and data obtained by means of expert interviews. The results confirm that in the first decades of the 21st century Russian print media paid less attention to the opportunities of media convergence than Western ones. At the same time, those Russian conventional media that set ambitious goals for their future considered the adoption of the media convergence approach as crucial, even before the pandemic. The findings show the lack of systemic measures to improve the overall situation on the national media market that faces difficult times, and the need to take into account pandemic risks in the print media management activities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".