Surviving a Border Apartheid in a Global Health Crisis: How (Un)Documented Afghans in Nowshera Navigate (In)Access to COVID-19 Vaccines and a Life in the Peripheries
Bibliographic record
Abstract
This thesis discusses the dimensions of border violence survived by (un)documented Afghan refugees and their descendants living in Nowshera, Pakistan.Focusing on the right to health and access to COVID-19 vaccines, I posit the pandemic as an analytical lens to demonstrate the (i) deathly function of borders, (ii) fragility of human rights regimes, and (iii) revolutionary power of community-based care for displaced communities in crisis.Using ethnography, individual interviews, and photovoice, I centre the lived experiences of (un)documented Afghans to reveal how they are pushed into an apartheid-like existence of intergenerational statelessness with perpetual precarious legal status, excluded from state-based regimes of care, denied access to COVID-19 vaccines, and faced with additional tyrannies navigating mechanisms of surveillance unique to the pandemic.Tracing practices of community-based care adopted by front-line healthcare workers, I demonstrate the revolutionary power of solidarity that helps Afghans survive and resist the deathly conditions of the border.
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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 | 0.002 | 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 | 0.016 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".