Bibliographic record
Abstract
Over the past two decades, human geneticists have substantially embraced the concept of “biogeographical ancestry” to account for the racial, ethnic, and linguistic categories they use to analyze and interpret genetic difference. Understanding the ongoing role of these categories in human genetic research therefore requires attention to geneticists’ representations of geography, particularly the geographic maps they use to illustrate gene distribution and migration. This article examines how the methods and imagery of international genetic geography and its major evolutionary narratives have reinforced or refashioned nationalist practices of geography in the Middle East. Geneticists simultaneously conceptualize the region’s physical space as both a historical “crossroads” of human migration and the birthplace of distinct gene sequences and civilizations, alternately blurring and sharpening the boundaries between Europe and Asia. Focusing on genetic research in Turkey and Iran, this paper analyzes how geneticists draw and interpret geographic maps of the region while selectively erasing or highlighting state borders. These genetic maps negotiate between the idealized aims of international projects to reconstruct human evolutionary history, and the reality of practicing science under the constraints of nation-state politics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".