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Kashmiri Namda Felts in the Collection of the MAE RAS

2024· article· W7117467756 on OpenAlexaboutno aff
Olga Merenkova

Bibliographic record

VenueKunstkamera · 2024
Typearticle
Language
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsKashmiriClothingQuarter (Canadian coin)CeylonEthnographyHandicraft

Abstract

fetched live from OpenAlex

Kashmir has long been famous for its skilled craftsmen who brilliantly perform intricate wood and stone carvings and create beautiful textiles. Bright, thin and at the same time warm, Kashmiri shawls are a desirable element of attire not only in India, but all over the world. Kashmiri Namda felt carpets are considered now as outstanding textile masterpieces; they occupy a special place in the world's museums and private collections. This study examines rare embroidered Namda felt carpets created in Kashmir in the late 19th and first quarter of the 20th century. In August-September 1916, in Kashmir, the spouses Alexander Mikhailovich and Lyudmila Aleksandrovna Meerwarth purchased a variety of wood and papier-mâché products, jewelry, fabrics, embroidery, as well as Namda felt carpets. The collecting work took place as part of a scientific expedition to Ceylon and India (1914–1918), organized by the Museum of Anthropology and Ethnography (MAE). This collection is kept today in the MAE RAS (MAE No. 3018). Kashmiri Namda felt carpets purchased by A. M. and L. A. Meerwarth, are compared with modern carpet products of this type. The art of Namda has been passed down by the families of makers from generation to generation. However, by the beginning of the 2000s Namda production declined as artisans and their families stopped practicing this craft. Currently, the authorities of Kashmir have recognized the traditional production of Namda felt carpets as a cultural heritage, therefore, projects aimed at training and supporting young carpet weavers are being actively developed and implemented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.296
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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