MétaCan
Menu
Back to cohort
Record W2620562331 · doi:10.24847/44i2017.109

Memorialization and Assimilation: Armenian Genocide Memorials in North America

2017· article· en· W2620562331 on OpenAlexaboutno aff
Laura Robson

Bibliographic record

VenueMashriq & Mahjar Journal of Middle East and North African Migration Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArmenianGenocideDiasporaMemorializationThe HolocaustJudaismPersecutionImmigrationHistoryDeportationPolitical scienceAncient historyGender studiesSociologyLawArchaeologyPolitics

Abstract

fetched live from OpenAlex

The Armenian National Institute lists forty-five Armenian genocide memorials in the United States and five more in Canada. Nearly all were built after 1980, with a significant majority appearing only after 2000. These memorials, which represent a considerable investment of time, energy, and money on the part of diasporic Armenian communities across the continent, followed quite deliberately on the pattern and rhetoric of the public Jewish American memorialization of the Holocaust that began in the 1970s. They tend to represent the Armenian diasporic story in toto as one of violent persecution, genocide, and rehabilitation within a white American immigrant sphere, with the purpose of projecting and promoting a fundamentally recognizable story about diaspora integration and accomplishment. This article argues that the decision publicly to represent the Armenian genocide as parallel to the Holocaust served as a mode of assimilation by attaching diaspora histories to an already­recognized narrative of European Jewish immigrant survival and assimilation, but also by disassociating Armenians from Middle Eastern diaspora communities facing considerable public backlash after the Iranian hostage crisis of 1980 and again after September 11, 2001.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.309
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueMashriq & Mahjar Journal of Middle East and North African Migration StudiesSame topicJewish and Middle Eastern StudiesFrench-language works237,207