Scapegoating and the simulation of mechanical solidarity in former Yugoslavia: “Ethnic cleansing” and the Serbian Orthodox Church. Humanity and Society 31(1): 65–82
Bibliographic record
Abstract
In this paper I use the concept of scapegoating to explain the ritualized character of "ethnic cleansing " after the break-up of Yugoslavia in the 1990s. I provide an overview of the political background behind these events, introduce the role and influence of the Serbian Orthodox Church, and analyze the collective violence known as ethnic cleansing through the concept of scapegoating. The Serbian Orthodox Church's use of a scapegoat paradigm to incite violence created a pseudo-sense of solidarity among the Serbian people. Although this solidarity resembles Émile Durkheim's concept of mechanical solidarity, I question the stability of this solidarity insofar as it is based on the negativity of war crimes and genocide. Implications for understanding collective violence in other areas such as the Middle East and Iraq are drawn by way of conclusion. REFLEXIVE STATEMENT My interest in the former Yugoslavia began in 1991. I was deeply disheartened by the disturbing reports of crimes against humanity. I read the New York Times daily and listened to the shortwave radio. When I taught my classes, I used these events to demonstrate and test various sociological principles. I started to organize sessions on Bosnia at conferences in Canada and the United States. In 1998 I was invited to a conference on Democracy in Multi-Ethnic Societies and Human Rights in Konjic, Bosnia-Herzegovina, and then the Bosnian Paradigm International Conference in Sarajevo. I befriended scholars with similar interests. In spring 2000 I received a Fulbright Lecture Award at the Faculty of
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".