Panel Two: Armenian Genocide – Politics and Precedents
Bibliographic record
Abstract
Moderator: Christian Axboe Nielsen (Aarhus University, Denmark) Edip Gölbaşı (Koç University Research Center for Anatolian Civilizations, Turkey and Simon Fraser University, Canada)The 1895-1896 Armenian Massacres in the Ottoman Eastern Provinces: A Prelude to Extermination or a Revolutionary Provocation?download paper (login required) Urban Jaksa (University of York, United Kingdom)Geopolitics of Genocide: Comparing the Ottoman and Russian Empires’ Ethnic Cleansing Policies against Armenians, Greeks, and Circassians in the Late 19th and Early 20th Centuriesdownload paper (login required) Varak Ketsamanian (University of Chicago)Genocide as a Colonial Tool: The Formation of the Armenian Legiondownload paper (login required) David Leupold (Humboldt University, Germany and Erasmus Mundus Grantee at Eurasian University, Armenia)Genocide Memorialization beyond the Ethno-National Divide: Bridging Memories of Armenians, Kurds and Turks in Van, Mush, and Sasun download paper (login required)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.008 |
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".