Local Government Response in Corona Pandemic: An Overview of Karnali Province
Bibliographic record
Abstract
The world was completely locked down for the first time in the world by the devastating pandemic of coronavirus in 2020. To overview its impacts on service delivery of local governments of Karnali province of Nepal, the study has been conducted. the paper attempts to explore the local government initiatives and inter-governmental coordination toward the COVID-19 response during the first wave. The study is based on a descriptive qualitative research design to reach the set objectives. Three local governments of Karnali Province were selected purposively as samples. Questionnaire and telephonic interviews were the tools adopted for the data collection. The field-level information and data were presented as per the objectives in a descriptive way. The study has been made to draw conclusions based on the practical experience and information gained from all three local levels. Local governments have been seen to be at the forefront of coping with the uncomfortable situation created by the Novel Corona Virus pandemic. Similarly, the local government seems to have performed important tasks such as manpower mobilization, procurement of health-related materials, test kits and machines, swab collection and test, and awareness rising toward covid-19 response and rescue of returnees from abroad. The local governments have found utilized the available resources for creating, managing the quarantine, and isolation at the community level to control the Covid-19. The local governments have learned the lessons from the field-level experiences in the pandemic management process in such types of crises. The study would be helpful to share the pandemic management knowledge among the local governments in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".