Bibliographic record
Abstract
Abstract The logics of ‘finance’ and ‘security’ have been enmeshed within each other in complicated ways since at least the start of the 20th century. As fields deeply alive to the possibilities and dangers associated with risk and uncertainty, finance and security occupy overlapping but uneven fields of operation. This article examines one particular financial mechanism – political prediction markets – in order to trace out the tensions and intersections of finance and security in one particular site. Political prediction markets are designed to harness the predictive power of the market to address an inherently uncertain object – the weather, political events, terrorism, etc. A series of recent cases – most notoriously a proposal by the Pentagon to construct a ‘terrorism futures market’ – have sought to recast political prediction markets as a security practice and to enlist these markets in the ongoing ‘war on terror’. This article argues that these attempts at financializing security offer a particularly useful glimpse into one point of overlap between security and finance. As markets constructed to measure and manage uncertainty, experiments in security prediction markets foreclose political space not only as a ritual of securitization that places certain issues above or beyond political deliberation but also as a reinvocation of a conception of ‘finance’ as a somehow rational and technical domain. As the terrorism futures case reminds us, however, the rational ambitions associated with these two governmentalities of financialization and securitization can become corroded or can lose coherence in unpredictable ways. It is in the political tension that is generated through such corrosions that the future of these kinds of experiments in the financialization of security will ultimately be decided.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".