Networked Diasporic Memory Work of Assyrian Genocide Remembrance
Bibliographic record
Abstract
The Assyrian nation, indigenous to modern-day Iraq, Iran, Syria, and Turkey, was first forced into diaspora following the WWI-era genocide (the Seyfo) committed by the Ottoman Empire, in which an estimated 250,000 Assyrians were massacred, and tens of thousands of survivors were prevented from returning to what became the Republic of Turkey. Successive waves of persecution, violence, and ethnic cleansing over the following century further contributed to Assyrian flight from their homeland states, and ultimately the formation of diaspora communities in the United States, Canada, Australia, and Europe. In this article, we explore how diasporic memory agents utilize social media to make visible the still largely forgotten and marginalized Assyrian Genocide amongst the diaspora, in kinship with other communities that survived Ottoman genocide, and to the general public. Our study examines the use of social media, specifically Twitter (X) and Facebook, leading up to and on Assyrian Remembrance Day (also known as Assyrian Martyrs Day) on 7 August and Seyfo Remembrance Day (which also commemorates the start of the Armenian Genocide) on 24 April. Narratives of loss and resilience, alongside calls for recognition, help to shape a collective memory across the diverse and multi-generational diaspora, positioning genocide remembrance as a core pillar of Assyrian national memory. Such work also serves to institutionalize genocide remembrance with what we call networked memory work, the strategic use of social media networks by memory agents to transmit the memories and historical narratives of their group to outside organizations, individuals, and institutions so that those outside networks also become carriers of the group's memory, helping to navigate the diaspora through its trauma processing, a process we posit is still incomplete.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".