Mobilizing the police from the top down as public health partners in combatting COVID-19: A perspective from Vietnam
Bibliographic record
Abstract
The coronavirus (COVID-19) was declared a global pandemic by the World Health Organization on 11 March 2020.The pandemic is having a profound impact on global order, the global economy, and the health and well-being of millions of people across the globe.As its impact continues to unfold, the relationship between public health capability and policing responses has become a focus of analysis.Vietnam has so far managed to avoid a health catastrophe, and, given its proximity to China, this bears further examination.COVID-19 was first documented in Vietnam on 23 January 2020, when Ho Chi Minh City officials reported two confirmed cases involving people who had recently travelled from Wuhan, in Hubei province, China.At the time of writing, the last case recorded was "patient number 268," with 223 patients making a full recovery and no death toll after three months (Minh & Bich, 2020;Viet-Phuong et al., 2020).Most COVID-19 cases have involved people travelling to Vietnam from overseas.Vietnam's effective response to COVID-19 is founded on its experience with the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak, which involved significant collaboration with international agencies and foreign governments (Lucius, 2009).
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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 | 0.005 | 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 | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".