Bibliographic record
Abstract
Although popular culture has become an important area of study in international relations, few scholars so far have turned their attention to popular romance fiction, despite its popularity among readers. Through an analysis of contemporary category romances featuring military heroes, and combining the scholarship on popular romance fiction with that of security studies, I open a new area of study for scholars of security. I argue that the structure of the romance genre – which requires the hero and heroine to fall and love and be happy at the end of the novel – reinforces particular kinds of politics. First, the focus on intimate relationships closes off broader critiques of global politics. Second, the focus on the home front reinforces the idea that there is no possible distinction between a peaceful home front to be protected and an international space of war. Third, heroes dealing with grief, post-traumatic stress disorder (PTSD), and other problems of a return to civilian life after deployment are portrayed as turning chaos into quest, again through a courtship narrative. Because of the familiar settings and stories that ‘feel true’, popular romance fiction is a site for the reproduction of specific kinds of military masculinity and military families. While these fictional accounts can have the beneficial effect of providing more nuanced portrayals of possible intimate lives of soldiers, they also close off critiques of politics and help to order a resilient, war-ready society and reinforce these images among readers who may not otherwise seek out non-fictional stories about the military.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".