А.L. Gorbunkov and the Fortune of Chukchi and Asian Eskimo Ivory Carving Art: Experience of Predecessors and Actual Trends
Bibliographic record
Abstract
The article deals with the methods of work of Chukchi and Asian Eskimo ivory cutters and engravers in art workshops since the 1930s. Particular attention is paid to the results of work with Chukchi and Asian Eskimos with A.L. Gorbunkov, the first artistic director of Chukchi ivory carving and fur-tailoring workshops, and is analysed the influence that Gorbunkov exerted on the further development of Chukchi and Asian Eskimo ivory carving art. The author consistently investigates each stage of life of Chukchi and Asian Eskimo ivory carving workshops after Gorbunkov’s return to the mainland: under the leadership of V.M. Leont’ev, I.P. Lavrov, during business trips to the Chukchi Peninsula of the specialists from the Research Institute of Art Industry, as well as the specific characters of work of the Uelen ivory-carving workshop after the collapse of the Soviet Union. Field materials gathered during the expeditions to Chukotka in 2023–2024 provide an objective assessment of the current state of ivory-carving art in Chukchi art workshops. The analysis of the work of Chukchi craftsmen over the century leads to the conclusion that the key role in the development of ivory-carving craft was played by art directors, and the most effective model is the work of local craftsmen in tandem with professional artists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".