Genocide and Identity (Geo)Politics: Bridging State Reasoning and Diaspora Activism
Bibliographic record
Abstract
Since the independence of Armenia in 1991, the question of whether and how to include the Armenian Genocide on the state’s foreign policy agenda has become the most important issue of controversy between the republic and the global Armenian diaspora. International recognition of the genocide and demands for reparations have been central to diaspora activism and have defined what experts conceptualize as “identity politics.” The Armenian state, however, has been reluctant to include the issue on its political agenda. Eager to establish diplomatic relations with Turkey and open their shared border—closed since 1993—for trade and economic development, Yerevan has insisted on “relations without preconditions” with Ankara. There is, therefore, a clear gap between the state reasoning and diaspora activism. This paper looks at identity politics and state reasoning through the lenses of international relations theory to examine the divide between the two parties and how it might be bridged. It employs Yossi Shain’s framework of diaspora politics to study the relationship between the Armenian diaspora and state concerning the question of the genocide. It argues that an area of convergence followed the failure of the Armenian-Turkish agreement of 2009, which is evidence of an ongoing social construction of identity geopolitics toward a bridging of the gap.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".