Regime change and Crisis Management in Tanzania: A Comparative Study on Managing COVID-19 Pandemic in Dar es Salaam.
Bibliographic record
Abstract
The first quarter of the 21st century has witnessed a number of crises, from terrorism to forced migration, and increased digitalisation to epidemics and pandemics. How will a government manage to effectively control such threats for a sanito-economic and socio-political well-being of its population? This study assessed the ability of democratic governance to control crises; the aim being to determine effective measures to be taken in the fight against such crises like COVID-19 pandemic. Using a comparative analysis, this study scrutinized measures adopted by both, the fifth and sixth Tanzania regimes in the fight against SARS-CoV-2 infections and deaths in Dar es Salaam. A total of 370 respondents from Ilala, Kinondoni, and Ubungo municipalities participated in this study. Also, literature survey and documentary analysis were carried out in order to get informed assumptions and people’s preferences. The research found a correlation between tough measures and effective curbing of COVID-19 pandemic. Further studies are needed to establish a causal relationship between exceptionally tough measures and population safety and well�being in critical situations. \nKeywords: Democratic governance, COVID-19 Pandemic, Tough measures,Population safety
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".