Building Hope in Crisis: Global Public Service Broadcaster Innovation During the Covid-19 Pandemic
Bibliographic record
Abstract
This paper aims to explain the role of Public Broadcasting Institutions around the world in delivering accurate and educational information to the public about the Corona virus outbreak which has become a global pandemic. This research uses a literature research approach by collecting various literature such as scientific journals, government reports, NGO reports, and annual reports of Public Broadcasting Institutions. This study uses a qualitative inductive logic-based content analysis method offered by Phillips and Marrying. The findings in this paper illustrate that Public Service Broadcasting, which is a broadcasting institution that must exist in every sovereign country, plays an important role as a public media in reporting, disseminating information, educating and persuading the public to jointly take care of themselves and fight the coronavirus which has become a global pandemic with the following approaches: First, Série Le virus (Senegal) presents drama as a strategy to attract public interest in understand the impact of Covid-19. Second, RAI (Italy) launched a special program called 'Unno Mattina'. Third, the Australian Broadcasting Corporation (ABC) adopts a factual approach in reporting the Coronavirus by relying on verified information. In addition, ABC also broadcasts news and information about the development of the Corona virus with a very high volume. Fourth, the Korean Broadcasting System (KBS) provides reliable information through the operation of the Integrated News Room service for 24 hours a day for COVID-19 since March 4, 2020. The main goal is to provide complete, trusted, and in-depth information to citizens. Fifth, CBC Radio (Canada) aired the series 'The Current' which explains the social and economic impact of the Corona virus in daily life.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".