When the Guest becomes the Host: Review of <i>Familiar Strangers: The Georgian Diaspora and the Evolution of the Soviet Empire</i>
Bibliographic record
Abstract
Erik Scott’s book Familiar Strangers begins with a tantalizing paradox: How did Georgians, a small people numerically, come to play a role as internal diaspora out of all proportion to their numbers in the Soviet Union from start to finish? I argue that in the thread that ties together the many examples of Georgian ethnic strategies (including the changing, but continuous, presence of Georgians in political and cultural life of the Soviet Union), Scott rightly focuses on the varied affordances of the Georgian table, both the “edible ethnicity” of Georgian food and wine but also the traditions of hospitality centered on this commensality and the forms of networking arising from it, which took hold in Soviet Culture beginning with Stalin. When Soviet citizens became guests at the Georgian table, a paradoxical inversion of guest-host relations occurred, so that the whole Soviet Union became, in effect, the guests of Georgian hosts. As Scott argues, it was precisely through making their own food, drink, and attendant rituals of hospitality central to Soviet rule and Soviet life that Georgians moved from being metaphoric ethnic guests in a host society to hosts within the imperial capital itself.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".