A Theory of Victimhood: Politics, Conflict and the Construction of Victim-based Identity
Bibliographic record
Abstract
What (and who) is a victim? In contemporary violent conflicts, the construction of grievance-based identity is a fundamentally contested process as the lines between victim and perpetrator are blurred by ongoing cycles of belligerence and retribution. As victims are incorporated into broader political campaigns, it becomes nearly impossible to separate the victim from the politics. The ubiquity of victims in international politics is a serious challenge to International Relations theory as categories of victim and perpetrator are generally treated as ‘prior or external to analysis’ instead of as propositions for further inquiry. This article formulates a political theory of victimhood driven by a distinction between victimisation as an act of harm perpetrated against a person or group, and victimhood as a form of collective identity based on that harm. It proposes a sequence of five stages that victims experience from the act of victimisation to the recognition of victim-based identity: (1) structural conduciveness, (2) political consciousness, (3) ideological concurrence, (4) political mobilisation and (5) political recognition. The article explores the stages with concrete examples and offers three main challenges for future research. First, as an identity, victimhood is more prominent in societies that recognise justice. Second, victimhood accompanies struggles for recognition. Third, victim rivalries obfuscate straightforward analysis of victimhood in conflict zones.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".