Beyond Agency as Good: Complicity and Displacement after the Siege of Sarajevo
Bibliographic record
Abstract
Abstract Forced migration scholars have increasingly documented the agency of displaced persons. However, this scholarship has attended primarily to the positive or constructive dimensions of agency, documenting migrants’ capacities for resilience, resistance, and problem-solving. In this paper, I argue that forced migration scholarship should extend to recognize the darker dimensions of agency, such as complicity in acts of violence. Drawing on emerging work on ‘complex victimhood’ in conflict studies scholarship, which grapples with the difficult simultaneity of victimhood and complicity, I begin to articulate a figure of the ‘complex migrant’. As a case study, I draw on fieldwork with Bosnian Serb women who were part of the 1996 displacement of Serbs from Sarajevo, when the divided city was re-unified following nearly four years of siege by Bosnian Serb forces. Against the figure of the ideal refugee/victim, I outline the numerous deviations that made Serbs illegible as refugees. I also demonstrate how my interlocutors asserted the qualities of the ideal victim in their narratives to make their losses legible. I argue that a complex victimhood framework is useful for analysing other understudied retributive displacements. I also suggest that it can work to gradually disempower discourses that blame migrants when they fail to live up to the ideal of the good victim.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".