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Record W3091548492 · doi:10.24043/isj.131

237 Islands of memory, islands of trauma: The case of Dongzhou, Hengyang, China

2020· article· en· W3091548492 on OpenAlexvenueno aff
Hong Gang

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersNational Office for Philosophy and Social Sciences
KeywordsWitnessChinaHistorySmall islandIsolation (microbiology)GeographyArchaeologyPolitical science

Abstract

fetched live from OpenAlex

This paper examines the complex interaction between island histories and island geographies by presenting a case study of Dongzhou Island, Hengyang, China, contextualized through several critical island events. Employing a wide range of methods including archival research, textual and media analysis, field and map observations, semi-structured interviews, and informal interaction, the research is broadly framed in the problematic of geographical memory consisting of hard island memory, soft island memory, and lived island memory. Particular attention is paid to the construction of Dongzhou Island’s cultural trauma based on three difficult island histories: mass killing, radical planning, and uneven development. Findings indicate that these memories coexist on or about the island, in virtual isolation from each other. It is argued that while hard memory constructs the island as a petrified landmark, soft memories murmur about the forgotten or obscured pains of a small island that bears witness to the violence of war and progress. The theoretical and practical implications regarding the role of place memory in rejuvenating local cultures are also proposed.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.041
GPT teacher head0.320
Teacher spread0.279 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

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