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Record W3158386524 · doi:10.24043/isj.158

Lowland islands at the water-land nexus in Lixiahe, China: A boundary approach

2021· article· en· W3158386524 on OpenAlexvenueno aff
Dongxue Lei

Bibliographic record

VenueIsland Studies Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersChina Scholarship CouncilNanjing UniversityGovernment of Jiangsu Province
KeywordsNexus (standard)ModernityGeographyNatural (archaeology)Context (archaeology)ChinaPsychological resilienceBoundary (topology)EcologyEnvironmental ethicsSociologyArchaeologyEpistemologyEngineering

Abstract

fetched live from OpenAlex

Historically known as China’s ‘Netherlands’, the lowlands of Lixiahe were and still are characterized by vast waterscapes. This paper introduces an island society fostered by this wet landscape, which thrived in premodern times and has undergone a transition into modernity. From a long-term perspective, there have been constant interactions between this island society and the water-land environments. This study details such socio-natural interactions and reconsiders the role of natural settings in the evolution of this island society in the modern context of intensifying human interventions. A comparative study is conducted in two periods of premodern and modern times, and a parallel examination is conducted into three levels of this island society to explore island relations. A boundary approach based on landscape ecology theories is employed to interpret the complex socio-natural interactions in both temporal and spatial dimensions. Through this historical exploration, the paper concludes with three links between resilience thinking and architecture/planning practice in wet landscapes and discusses contemporary issues connected with the socio-ecological resilience of these lowland islands.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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