<i>Parrhēsia</i>Today: Drone Strikes, Fearless Speech and the Contentious Politics of Security
Bibliographic record
Abstract
Foucault is more often used to theorise political logics of securitisation than to understand the contestation of security policies. Yet Foucault's work offers a wealth of conceptual tools and ideas pertinent to the study of the contentious politics of security. In his lectures on parrhēsia in Ancient Greece, Foucault explored the practice whereby individuals choose at great risk to confront rulers or publics with uncomfortable truths. This article argues that a refashioned concept of parrhēsia can illuminate certain elements of the contentious politics of security today. The article develops this claim through an examination of the photojournalism of Noor Behram, a man who has spent four years photographing and exposing US drone strikes in the region of Waziristan in Pakistan. The article analyses Behram's activity in terms of parrhesiastic exposure, a concept that is intended to capture aspects of the changed circumstances under which fearless speech can be exercised in mass-mediated, globalised societies. The article concludes by observing how further engagement with parrhēsia might contribute to our understanding of political action, the relationship between emotions and political struggle, and the politics of secrecy.
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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 | 0.001 | 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 | 0.006 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".