Government-Owned Digital Services to Overcome the Spread of COVID-19, Case in Indonesia
Bibliographic record
Abstract
The Indonesian government faces challenges in running public services during the COVID-19 pandemic, the pressure to implement digital-based service solutions so that public affairs within the government-run.Our research analyzes social media discourse to understand the joint production of digital-based public services during the COVID-19 pandemic.Our research uses a qualitative method, using a netnographic method approach that is referenced from the Twitter social media data set and analyzed using discourse as a flow to analyze citizen responses to the contact tracer application (CTA) (pedulilindungi.id) owned by the Indonesian government through the Ministry of Health of the Republic of Indonesia in minimizing risks.Our research contributes to the accountability sector for digital-based public services.It provides a scientific understanding of public trust in influencing the development of coproduction of digital-based services.This study found a high public sentiment toward the care protection application and a lack of trust in the government's actions in overcoming the COVID-19 problem, especially running CTA.Public responses from Twitter users express disappointment and doubt that data is always not updated.In addition, the digital divide is a problem faced by the public, who have little understanding of the care-protected application services.In the end, we realized that this research has limitations in capturing the public's response directly outside social media to implement digital-based service co-production.We recommend further research to see the public reaction from other approaches, such as social media outside of Twitter.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".