Shaking the Ground: Intersections of Cultural Trauma, Collective Memory, and Resilience in Assyrian Popular Music and Dance
Bibliographic record
Abstract
This dissertation is a study of the interstices between cultural trauma and collective memory in diasporic Assyrian popular music and dance expressions. An ethnic and religious minority from the converging borders of Syria, Turkey, Iraq, and Iran, the Assyrians have experienced repeated persecution resulting in forced migration and refugee flight, most recently with the rise of the Islamic State of Iraq and Syria (ISIS) in 2014. The majority of Assyrians now live in diaspora, with significant communities in North America, Europe, and Australia. Focusing on the North American diaspora, this research examines how shared trauma and memory form the foundation of an imagined Assyrian nation and collective consciousness. Contributing to scholarship on forced migration, refugee flight, and community formation in diaspora, this dissertation draws from primary source data gathered through multi-sited, ethnographic fieldwork in the cities and surrounding areas of Toronto, Ontario; Chicago, Illinois; San Jose, California; and Scottsdale, Arizona. This study aims to discern the nuances of how people maintain integrity and produce, or reproduce, cultural identity in the wake of destructive forces. How have music and dance been used to communicate the (hi)story of Assyrians, with its absence in state histories, scholarly literature, and mainstream media? When physical place is not tenable as the foundation for group identity, why – and with what understandings – do Assyrians in diaspora turn to music and dance as sites for expressing or contesting collective identities? How can the expressive culture of the Assyrian community (re)place or (re)build what has been lost, and what purposes do music and dance serve for (re)constructing collectively imagined identity under circumstances of mass dispersal and displacement? Through the analysis of (1) Assyrian popular songs, (2) semi-formal interviews and participant-observation at community events, and (3) dance as embodied knowledge, my research demonstrates how migration and resettlement can bring a resolve for Assyrians to survive, celebrating or competing through cultural practices that express emergent Assyrian identities in and across their new lifeworlds.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".