Bibliographic record
Abstract
Hagop Bogigian is the first person to immigrate to America from Huseynik. He is also the “first Armenian-American millionaire”. The story of Bogigian, who set out with the desire to immigrate to America at the age of 19, is of the kind that encourages those who immigrated after him. It is important to follow the traces of his life story even from this aspect alone. It is possible to encounter the characteristic features of the migration movement in many aspects such as the reason for the individual request for immigration, going to America, the aids he received, how and where he settled in America, what he was engaged in. Bogigian, who met the "New World" in Harput under the influence of American missionaries, is not just an ordinary immigrant. Later, as an Armenian-American importer and exporter, he transferred technological innovations to the Ottoman lands, especially to the east. He used all his political and financial means and connections to the Armenian immigrants who came to America like him. Again, the thought and work of Armenians living in a designated area in Canada and America with the idea of an "Armenian colony" is remarkable. After his death, he left his legacy as the “Hagop Bogigian Scholarship Fund” to the education of Armenian students in need, especially women, at Wilson College, Mount Holyoke College and Pomona College.The life story of Hagop Bogigian, whose footprints of the first Armenian migration from Hüseynik to America will be traced, is the subject of this study. Bogigian's life is also a representative story that carries important clues to understand the transatlantic migration that started from Harput.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.013 |
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".