Bibliographic record
Abstract
The figure of the soldier-migrant demonstrates why it is important to bring the question of military service into contemporary sociological debates about citizenship, belonging and racism. The article draws on an understanding of whiteness as a fundamental component of historical and gendered notions of citizenship that feed the ‘hypnotic ideals’ of national identity. Because of academic specialism and disciplinary boundaries, however, the intersections between civil and military spheres are often neglected as a locus for exploring racialized terms of belonging and exclusion, particularly in times of war. The article discusses key questions raised by the campaign for Gurkha settlement rights and the employment of thousands of personnel from Commonwealth countries in the British Army, bringing the notion of whiteness as ‘fitness for citizenship’ into dialogue with recent work on the soldier-citizen developed in Canada and the USA. Recent British National Party (BNP) propaganda demonstrates the perils of leaving the link between military service and the indigenous ‘deserving’ Brit undisturbed, and the concept of postcolonial melancholia remains a vital way to approach the mobilization of war memories as a way of defining the terms of UK citizenship today
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".