Post-Pandemic Governance: Evaluating Emergency Communication Regulatory Policies in Local Government Responses to COVID-19 in Indonesia
Bibliographic record
Abstract
The COVID-19 pandemic has significantly altered the framework of global governance, particularly in the realm of decentralized governance, thereby impacting the social, legal, political, and public communication paradigms within Riau Province.This research aims to assess the effectiveness of supported emergency policies in strengthening governance agility and public trust during the crisis.The methodological approach adopted is qualitative, employing a descriptive framework that integrates primary data derived from interviews and field observations alongside secondary data sourced from official documentation.The findings indicate that initiatives such as large scale social restrictions and vaccination campaigns were instituted as a countermeasure to the health emergency.Nevertheless, these initiatives frequently exhibit a lack of transparency, leading to legal ambiguities and community resistance attributable to economic ramifications.Social disruptions are furthermore manifested through mobility constraints, digital disparities, and fluctuations in information dissemination that undermine the efficacy of public communication.The implications of this research may serve as a foundation for the formulation of economic recovery strategies, enhancement of the healthcare infrastructure, and the fortification of digital literacy in preparation for prospective pandemic challenges.This study offers a basis for policymakers to devise adaptive strategies in the context of a global crisis.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".