“To me, you are not a Serb”: Ethnicity, ambiguity, and anxiety in post-war Sarajevo
Bibliographic record
Abstract
The siege of Sarajevo has altered the experience of ethnicity, reconfiguring ethnic categories into moral boundaries. From 1992 to 1995, the city was held under siege by the Army of Republika Srpska, and many Sarajevan Serbs still grapple today with the feeling that others view them as aggressors. Based on one year of ethnographic fieldwork with Serb women of the pre-war generations, I describe how they intentionally make small alterations in gesture and body language in order to perform ethnic ambiguity, and avoid being read by others as Serb. While anthropological accounts have tended to use performativity to emphasize the constructed and situational nature of ethnicity, here I focus on the anxiety that drives Serb women’s performances in order to capture the inherent and inescapable feeling of ethnicity in a post-war space. I also discuss the difficulty of capturing this anxiety through empirical methods, navigating the discrepancy between Serb women’s narrative accounts of ethnic stigmatization compared to the apparently unproblematic flow of everyday social life. Through this discrepancy, I demonstrate how the embodied and ever-accumulating feeling of ethnic anxiety can conjure threats where there may be none, and how it can charge even the most (seemingly) mundane encounters.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".