Հիշողությունը և տրավման տապանագրային ու սգերգային ժառանգության համատեքստում
Bibliographic record
Abstract
This article examines cemeteries formed after the 1988 Spitak earthquake as unique cultural archives that have preserved multi-layered narratives of personal and collective memory, grief, faith, and identity. Drawing on local fieldwork, photographs, and ethnographic data, the study examines how cemeteries have become sites of memory, reflecting not only individual tragedies but also the process of rebuilding public identity. For the first time, the epigraphic and oral funeral legacies of Gyumri are combined, viewing them as a complementary memory system that unites material and non-material culture. This approach allows us to identify models of post-disaster memory formation that can be applied to other regions. Methods and materials: The research uses an interdisciplinary approach, combining the methods of cultural and narrative analysis to reveal the interrelationships between personal memories, symbolic images, and the formation of public memory. During the research, we used field folklore materials recorded by us /-K.S. and R.H./, which include local traditions, artistic inscriptions on tombstones, and oral narratives of disaster memory. Field studies were conducted after the 1988 earthquake in the old and new cemeteries of Shirak and Gyumri. Analysis: Within the framework of narrative analysis, the texts of tombstones, their symbolism, and iconography were studied. Results. The results of the analysis show that the epigraphic and oral funeral heritage of Gyumri reflects not only the general patterns of post-disaster cultural memory, but also a unique local model, which can be characterized as a dual memory system. This system is formed by the complementary interaction of material and non-material memory. Authors' contribution: They jointly wrote and systematized a large amount of material related to cemetery culture, tombstone art and oral narratives, conducted a study to find out how cemeteries, as cultural archives, preserve our identity and memory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".