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Rare collection about kashmir

2013· article· en· W1499739934 on OpenAlexaboutno aff
Rosy Jan, Shahina Islam, Uzma Qadri

Bibliographic record

VenueBrazilian Journal of Information Science research trends · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsTreasureSubject (documents)Library scienceHistoryCollection developmentGeographyClassicsArchaeologyComputer science

Abstract

fetched live from OpenAlex

Kashmir has been a fascinating subject for authors and analysts. Volumes have been documented and published about its multi-faceted aspects in varied forms like manuscripts, rare books and images available in a number of institutions, libraries and museums worldwide. The study explores the institutions and libraries worldwide possessing rare books (published before 1920) about Kashmir using online survey method and documents their bibliographical details. The study aims to analyze subject, chronology and country wise collection strength. The study shows that the maximum collection of the rare books is on travelogue 32.48% followed by Shaivism 8.7%. While as the collection on other subjects lies in the range of 2.54%-5.53% with least of 2.54% on Grammar. Literature of 20th century is preserved by maximum of libraries (53.89%) followed by 19th century (44.93%), 18th century (1.08%) and 17th century (0.09%) and none of the library except Cambridge University library possesses a publication of 17th century. The treasure of rare books lies maximum in United States of America (56.7%) followed by Great Britain (35%), Canada(6%), Australia (1.8%) with least in Thailand (0.45%).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.012
Science and technology studies0.0060.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0290.004

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 head0.056
GPT teacher head0.406
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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Same venueBrazilian Journal of Information Science research trendsSame topicSouth Asian Studies and ConflictsFrench-language works237,207