Using the Pandemic for Their Own Gain: The Experiences of COVID in Serbia
Bibliographic record
Abstract
The paper analyzes the experiences of older adults (65 years of age and older) with the COVID-19 pandemic in Serbia. At the beginning of the pandemic, the Serbian government declared a national state of emergency, which included a strict curfew in which older adults were forbidden to leave their homes under any circumstances. After 52 days, the state of emergency was lifted, which was soon followed by a rapid loosening of coronavirus measures. During this time, Serbia held parliamentary elections that were rife with irregularities. The government was accused of using the pandemic for political gains, including fabricating the numbers of COVID-19 deaths. The interlocutors in the study mapped their experiences with these measures and recounted how their lives had changed since the early days of the pandemic and into 2022. All interlocutors chose to frame their experiences through their criticism of the government and how it mishandled the pandemic. Rather than making excuses for a weaker government, the criticism is based on interlocutors’ expectations of a capable statecraft that can take care of its people, and the inability of the existing government to fulfill these expectations. Through the experiences of the pandemic, the study examines the tensions between the government and people in Serbia’s post-socialist context, and how these tensions are heightened during the time of crisis.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".