Pandemic Lessons for Democracies: High Time to Provide Journalism as Essential Service with a Financial Lifeline
Bibliographic record
Abstract
Summary The COVID-19 crisis has revealed a steady demand for professional journalism as an essential public service. However, the disfunction of the conventional advertising-supported business model has affected an overwhelming proportion of the industry workforce. This article contributes to the discussion on thinkable solutions. It argues that the pandemic has created further empirical evidence to support Habermasian ideas of providing a lifeline for the quality press as a vital contributor to the public sphere, a pillar of good governance in Western democracies. Amid the global challenge posed by the emergency, professional news organizations have proven their essentiality as providers of reliable information vital to tackle healthcare system and policymaking tasks. However, the legacy media are progressively less able to perform their social functions, losing the competition for revenue to the Big Tech. Therefore, liberal democracies should fund independent journalism to ensure the latter remains strong in the post-coronavirus world, holding the political systems accountable. The paper concludes that the pandemic has fostered an environment conducive to translating the feasible policy options into concrete political steps, regulation and lawmaking.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| 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".