Weapons of mass participation: Social media, violence entrepreneurs, and the politics of crowdfunding for war
Bibliographic record
Abstract
Since 2012, North American and European civilians have regularly engaged in combat operations against the Islamic State in the globalized and decentralized battlefields of Iraq and Syria. This article focuses on two aspects of this phenomenon. First, I argue that these combatants represent a different kind of fighter from both private military contractors and battlefield laborers profiled in the private security literature insofar as capital is a means rather than an end in the innovation of violence. I refer to these fighters as violence entrepreneurs. The relevance and limits of Schmitt’s writings on enmity and his theory of the partisan are examined in the context of these contemporary networks of security, mobility, and killing. My second argument centers on how online platforms for the distribution of small-scale donations to these fighters and their self-crafted missions facilitate hyper-mediated forms of patronage, where individual donors are both producers and consumers of security in ways that further distort distinctions between civilians and combatants. The imagined communities that support these combatants, both morally and financially, through the banal networks of Facebook and peer-to-peer funding platforms like GoFundMe suggest a radical deviation from conventional organizational structures and capacities for waging combat. Crowdfunding congeals these new geopolitical networks in the authorizing of individuals to determine their own singular forms of enmity, mutating the conditions of possibility for the sovereign decision.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".