Controlling the Narrative, Consolidating Power:COVID-19 and Indonesia's Deepening Democratic Crisis
Bibliographic record
Abstract
In mid-2022, Indonesia looked back at two and a half years of managing the COVID-19 \npandemic. In retrospect, there were two major, and very different, periods in Indonesia’s \napproach to COVID-19. The first period, from early 2020 to mid-2021, was marked by the \ninitial denial of the pandemic’s existence in Indonesia (Mietzner, 2020); the reluctance of the \ngovernment to impose stringent public health measures (Jaffrey, 2020; Aspinall, 2021); and the \nsystematic ignoring of warnings by epidemiologists and economists that the government’s \nprioritization of the economy was neither protecting the public nor the economy (Sulaiman, \n2020). After a massive spike of the Delta variant in mid-2021, which cost hundreds of \nthousands of people their lives and for which the government’s approach was primarily \nresponsible, Indonesia’s leadership changed course. Obviously shocked by the carnage, the \ngovernment tightened regulations, and it accelerated the acquisition of vaccines (Jaffrey, 2021). \nAs a result of natural protection caused by the mid-2021 wave and the new government \nmeasures (including a successful vaccination drive), COVID-19 fatality numbers remained \nrelatively low for the last quarter of 2021 and much of 2022.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".