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Record W7116337197 · doi:10.47205/jdss.2025(6-iii)05

A Cross-Regional Study of Built Heritage Conservation in South Asia within the Global Heritage Discourse

2025· article· W7116337197 on OpenAlexaff
Sana Younus, Saima Gulzar, Faiqa Khan

Bibliographic record

VenueJournal of Development and Social Sciences · 2025
Typearticle
Language
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsKeyano College
Fundersnot available
KeywordsGlobeContext (archaeology)Industrial heritageCultural heritageCultural heritage managementGlobal SouthSouth asiaWorld heritage

Abstract

fetched live from OpenAlex

This paper explores the built heritage conservation perspectives around the globe while analysing the international charters and local practices in the South Asian region.The international charters provided extensive guidelines for the protection of heritage while following procedures. The implementation in diverse regional and cultural context still requires additional research and formulation of procedures. The study employed two phase qualitative methodology starting from the literature mapping for the synthesis of key trends and paradigms in global heritage discourse, highlighting the material-centric conservation to inclusive community engaged approaches for sustainability. The second stage analysis of global case studies to explore the operational challenges within the South Asian context. The finding reveals that in this part of the world the global heritage frameworks are embedded in conservation practice but facing severe issue due to the socio-cultural, political and historical factors in addition to the economic instability. The paper concludes with the recommendations for application of context-sensitive approach embedded with the traditional aspect and the global construct.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0080.009
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.119
GPT teacher head0.340
Teacher spread0.220 · 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 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
Published2025
Admission routes1
Has abstractyes

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