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Record W4408600647 · doi:10.24043/001c.132354

Memory and Forgetfulness of the Flood: Meaning and Nostalgia in Thousand Island Lake (Qiandaohu), China

2025· article· en· W4408600647 on OpenAlexvenueno aff
Cao Li, Adam Grydehøj

Bibliographic record

VenueIsland Studies Journal · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythChinaMeaning (existential)HistoryGeographyArchaeologyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Islands are often associated with sites of memory, forgetting, and nostalgia. People find islands in the world and imbue them with social and cultural meaning. Drawing upon studies of islands as sites of memory and forgetting, and taking the case of Thousand-Island Lake (Qiandaohu) in Zhejiang, China, this paper argues that it is important to denaturalise island geographies when considering the social and cultural roles they play. Thousand-Island Lake is a result of the construction of Xin’anjiang Dam and Reservoir in 1958-1962, which flooded Chun’an Valley, submerging Lion City and transforming the surrounding mountain peaks into lake islands. Having developed into a tourist destination in the 1980s, Thousand-Island Lake has become a site for nostalgic heritage. The submergence of Lion City at the bottom of the lake has saved it from the fate of so many modernised Chinese cities and paradoxically made it emotionally accessible for nostalgic memorialisation. Dragon Mountain Island and Honey Mountain Island have accrued new meanings as islanded heritage sites, while numerous other lake islands have been given narrow and changeable tourist-oriented themes. The need for connection with a reconstructed past and the requirements of the tourism industry have been important for the formation of islands as islands in Thousand-Island Lake.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.251
Teacher spread0.213 · 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 designObservational
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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