Drone strikes, <i>dingpolitik</i> and beyond: Furthering the debate on materiality and security
Bibliographic record
Abstract
Abstract Recent scholarship in critical security studies argues that matter matters because it is not an inert backdrop to social life but lively, affectively laden, active in the constitution of subjects, and capable of enabling and constraining security practices and processes. This article seeks to further the debate about materiality and security. Its main claim is that materials-oriented approaches to security typically focus on the place of materials and objects within technologies and assemblages of governance. Less often do they ask how materials and objects become entangled in political controversies, and how objects mediate issues of public concern. To bring publics and contentious politics more fully into the debate about the matter of security, the article engages with Latour’s work on politics, publics and things – or dingpolitik. It then connects the theme of dingpolitik to a particular controversy: Human Rights Watch’s investigation of Gaza civilians allegedly killed by Israeli drone-launched missiles in 2008–2009. Drawing three lessons from this case, the article explores how further conversation between dingpolitik and security studies can be mutually beneficial for both literatures.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".