Cultural Lenses and Biological Filters On What Makes a Hungarian in the Present and in the Distant Past
Bibliographic record
Abstract
The definition of a memoir is “an account of the personal experiences of an author.” This paper provides the reflections of a physical (biological) anthropologist specializing in the genetics of the Indigenous peoples of North America who was born in Hungary, raised in Canada, and served twelve years as president and vice chancellor of the University of Manitoba. This professional background may question the relevance of these reflections to Hungarian studies. However, issues raised by János Kenyeres, the keynote speaker of the 2019 American Hungarian Educators Association conference, in his examination of Hungarian identity manifest in Hungarian literature—specifically, regarding “essentialist thinking”—are related to fundamental issues about the nature of human diversity with which physical (biological) anthropologists have been grappling since the eighteenth century. In an era in which commercial genetic genealogical services promise to identify ancestors and ethnicity, and genetic studies of living peoples as well as archaeogenomic studies of skeletal remains seek to identify relationships, current perspectives on what does—or does not—constitute “the essence of an individual and the groups to which one belongs” are worth considering. Facts, wherever they occur, are subject to interpretation. It is the cultural interpretation that we give to genetic identity that imbues that concept with meaning. emoke.szathmary@umanitoba.ca
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.071 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".