The Humanitarian Work of the Near East Relief during the Armenian Genocide and the Contribution of Nova Scotia Nurse Sara Corning
Bibliographic record
Abstract
Abstract: The backdrop for this research note is the Armenian Genocide of the early 20th century. In this context, we examine the activities of the Near East Relief (NER), an American humanitarian organization that began its relief work during the Armenian Genocide, as well as Canada's responses. Furthermore, we focus on the humanitarian relief work of Red Cross nurse and Nova Scotia native Sara Corning. Her story is little known in Canada, except in her Nova Scotia birthplace and among Armenian and Greek communities. The research note provides an overview of her call to humanitarian service, her resilience in the face of extreme hardship, and resistance against what were the gender and societal norms of that era. Despite these challenges, Sara Corning devoted much of the years from 1919 to 1930 to relief work in Armenia, Greece, and Turkiye, a broader region which, in the early part of the 20th century, was devastated by military conflict and ethnic cleansing, and the massive, government mandated, forced migration of Greeks and Turks in 1923. Résumé: Cette note de recherche a pour toile de fond le génocide arménien du début du XXe siècle. Dans ce contexte, nous examinons les activités du Near East Relief (NER), une organisation humanitaire américaine qui a commencé son travail de secours pendant le génocide arménien, ainsi que les réponses du Canada. En outre, nous nous concentrons sur le travail d'aide humanitaire de Sara Corning, infirmière de la Croix-Rouge et originaire de la Nouvelle-Écosse. Son histoire est peu connue au Canada, sauf dans sa ville natale de la Nouvelle-Écosse et au sein des communautés arménienne et grecque. La note de recherche donne un aperçu de son appel au service humanitaire, de sa résilience face à des difficultés extrêmes et de sa résistance aux normes sociétales et de genre de l'époque. Malgré ces défis, Sara Corning a consacré une grande partie des années 1919 à 1930 à l'aide humanitaire en Arménie, en Grèce et en Turquie, une plus vaste région qui, au début du XXe siècle, a été dévastée par des conflits militaires et d'épurations ethniques, ainsi que par la migration massive forcée, des Grecs et des Turcs, mandatée par le gouvernement, en 1923.
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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 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".