Surveillance in Weak States: The Problem of Population Information in Afghanistan
Bibliographic record
Abstract
Abstract: Surveillance scholarship has long been focused on surveillance technologies in strong states. This article explores the technological challenges of governing Afghanistan, a weak state, where reliable population data do not exist. In assessing the ways governance is practiced in a country of “ghosts,” I show that the failure of the state in Afghanistan is linked to a chronic poverty of reliable information on the country’s population and geography. A weak state with limited access to reliable population data must use force instead of knowledge to govern the country. I also argue that the digital technologies of surveillance practiced by the Afghan state and the U.S. military to substitute for the lack of traditional forms of government data are not effective and cannot strengthen the state’s capacity to deliver services. In contributing to debates on surveillance and security, this article provides a technological critique of state failure in Afghanistan by highlighting the costs of poor population information. “Surveillance in Weak States: The Problem of Population Information in Afghanistan.” International Journal of Communication , vol. 13 (2019), pp. 4778–4794.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".