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Record W4405219844 · doi:10.5539/ach.v16n2p72

Historical Landscape Preservation along Beijing Central Axis: A Case Study of Yongdingmen Gate Reconstruction

2024· article· en· W4405219844 on OpenAlexvenueno aff
Zifan Wang, Yasufumi Uekita

Bibliographic record

VenueAsian Culture and History · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban and spatial planning
Canadian institutionsnot available
Fundersnot available
KeywordsBeijingCentral cityGeographyEnvironmental planningRegional scienceHistorySociologyPolitical scienceEnvironmental resource managementEnvironmental scienceArchaeologyChina

Abstract

fetched live from OpenAlex

This paper examines the reconstruction of Yongdingmen Gate as an aspect of restoring the historical landscape of the Beijing Central Axis, a 7.8-kilometer-long urban axis that has shaped Beijing’s cityscape for over 700 years. The reconstruction of Yongdingmen Gate along the Central Axis, demolished in the 1950s, is a key initiative to restore the continuity of the Central Axis and preserve its historical integrity. This study explores the justification behind the reconstruction, focusing on how it aims to revive the historical landscape and ensure the Central Axis’s coherence as a cultural and spatial entity. The research addresses the challenges and debates surrounding the reconstruction, including its alignment with UNESCO World Heritage criteria and its role in conveying China’s historical and cultural narratives. The paper concludes with reflections on the implications of this reconstruction for urban planning and heritage conservation and suggests future research directions to enhance understanding and practices in these fields.

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)0.001
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 score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0100.006
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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
Published2024
Admission routes1
Has abstractyes

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