Bibliographic record
Abstract
Abstract This article explores the political implications of opposition to war, focusing on the example of conscientious objection to military service. Conscientious objection is often treated as a fundamentally ethical issue; however, this article argues for centering questions of justice in analyses of responses to war. There is a risk that starting with ethics takes for granted the social significance of ethical responses and overlooks particular ethical practices in the reproduction of wider inequalities. More specifically, when an issue is narrowly framed in terms of ethics, it can have implications for who is allowed to speak and what they can speak about. Thinking about war as an issue of justice—in the sense of how society allocates the things that it values—allows the broader issues of hierarchy, distribution, and recognition to be in the foreground. The article focuses on the example of an Indian subject of the British Empire who applied for exemption from military service during the Second World War. The valorization on conscience by the British state prioritized a limited form of opposition to bloodshed grounded in personal moral scruples, to the exclusion of anti‐imperial self‐determination, and turned the war into an issue of individual ethics rather than global inequality.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".