Memory and Forgetfulness of the Flood: Meaning and Nostalgia in Thousand Island Lake (Qiandaohu), China
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".