Technologies of (in)security: Masculinity and the complexity of neoliberalism
Bibliographic record
Abstract
Although there is much feminist work that has examined the intersection of gender and neoliberalism, critical work on men and masculinities remains underdeveloped in this area. This article suggests that complexity theory is a crucial resource for a critical analysis of the ways in which masculinities contribute to the ongoing maintenance of neoliberal socio-economic systems. Critical work on neoliberalism and capitalist economics has recently been drawn to complex systems theory, as evidenced by the work of scholars such as Sylvia Walby, William Connolly and Brian Massumi. Their work produces important insights into neoliberalism, but does not develop a sustained reflection on the place of men and masculinities in this domain. In order to develop a critical account of the relation of masculinity to complexity, the article draws on the work of Judith Butler and Bonnie Mann. It suggests that Butler’s theorising on precariousness contains important resources for understanding how hegemonic masculinities are positioned in relation to the complexity of neoliberal systems, as illustrated in Mann’s concept of ‘sovereign masculinity’. Finally, drawing on two different examples of the enactment of masculinities in neoliberal contexts, the article argues that hegemonic forms of masculinity can be understood as technologies for the amelioration of the complexities and insecurities generated by neoliberal markets.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".