Collecting Ukrainian Heritage: Peter Orshinsky and Leonard Krawchuk
Bibliographic record
Abstract
Most discussions about collectors of folk art focus on financial issues, examining what makes an object valuable and worth collecting. But financial gain is not the primary motivation of all collectors. When it comes to folk art associated with heritage, collectors are driven by a desire to connect to a past. Often this is a past with which the collectors themselves had no direct contact, but one which they feel they need to understand in order to make sense of their own identity. Folk art objects make the past tangible; they allow a physical link to something that needs to be grasped to be understood. Peter Orshinsky and Leonard Krawchuk are two important collectors of Ukrainian folk art. Their lives provide instructive case studies that help us understand heritage collecting. La plupart des travaux sur les collectionneurs d’art populaire sont focalisés sur les problèmes financiers; on y étudie ce qui rend un objet précieux et digne d’être acquis. Mais le profit n’est pas la motivation principale des collectionneurs. Quand il s’agit d’art populaire associé à un patrimoine, c’est plutôt le désir de se connecter à un passé qui les y pousse. Il n’y a souvent rien de commun entre eux et ce passé, mais ils éprouvent le besoin de le comprendre afin de donner du sens à leur propre identité. Les objets d’art populaire donnent au passé une réalité que l’on peut toucher, ils permettent d’avoir un lien physique avec quelque chose que l’on doit saisir pour le comprendre. Peter Orshinsky et Leonard Krawchuk sont deux collectionneurs importants d’art populaire ukrainien. Leur vie nous fournit une étude de cas fort instructive qui nous aide à comprendre l’acquisition d’objets patrimoniaux.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".