The Politics of Pakistan’s Covid-19 Response: A State-in-Society Approach
Bibliographic record
Abstract
This paper takes a “state-in society” approach to understanding the evolution of Pakistan’s COVID-19 response, which was laid claim to and contested by multiple agencies within and adjacent to the state, and by multiple levels of government. The capacity of the health system of Pakistan was already overstretched by the needs of its population but in recent years it has been hamstrung by ongoing protests by the medical community concerning the privatization of public-sector hospitals, to which were added protests over the lack of personal protective equipment in the public sector. These protests resulted in frequent closures of outpatient departments at major hospitals. When the government announced a relief package to mitigate the effects of COVID-19, traders and big businesses lobbied the government to obtain the lion’s share in the form of concessions such as loan deferments and tax refunds. The government touted the unconditional cash grants program but the cash for the poor could not be disbursed effectively due to the absence of local governments at the grassroots level. As an appropriate response to the pandemic, especially in relation to the lockdown policies, was contested and negotiated among multiple actors in the Pakistani state and society, the Pakistani military emerged as a dominant force in this “field of power.”1 In this paper, I present an account of Pakistan’s response to COVID-19 as it evolved in 2020 and discuss the implications for democratic culture in Pakistan.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".