Genocide by Other Means: Heritage Destruction, National Narratives, and the Azeri Assault on the Indigenous Armenians of Karabakh
Bibliographic record
Abstract
The propaganda efforts of the authoritarian Aliyev regime in Baku and the general Western ignorance of the history of the South Caucasus have contributed to the lack of meaningful response to the genocidal aggression that Azerbaijan has inflicted on the indigenous Armenians of Artsakh, known to many as Nagorno-Karabakh. The humanitarian crisis created by the Azeri blockade of the Lachin Corridor is only the most recent step in a process of cleansing the region of its Armenian population, a process that began in the early years of the twentieth century. The Ottoman Turkish genocide of Armenians in 1915–1923 is not a distinct event of the past but a process whose ideology is central to the Azeri-Turkish genocidal violence perpetrated against Armenians in the present. An integral component of the processes of genocide is cultural heritage destruction as noted by Raphael Lemkin. The erasure of most signs of the indigenous Armenian presence on its historic homeland was particularly pronounced in the decades following the Armenian Genocide and continues today. Cultural erasure went hand in hand with Turkish state genocide denial and the rewriting and mythologizing of its national narrative. Azerbaijan has been following a similar playbook since the collapse of the Soviet Union. These genocidal processes of denial, heritage destruction, and the rewriting of history are what I describe as “genocide by other means.”
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".