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Record W2331137213 · doi:10.5509/2005782195

Ruining and Restoring Rivers: The State and Civil Society in Japan

2005· article· en· W2331137213 on OpenAlexvenueno aff
Paul Waley

Bibliographic record

VenuePacific Affairs · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCivil societyState (computer science)Political scienceEnvironmental ethicsPolitical economySociologyLawPhilosophyPoliticsComputer science

Abstract

fetched live from OpenAlex

R ivers in Japan and Currents in Civil Society The postwar history of Japan can be seen in terms of the inexorable march of development through construction (generally in concrete).1 Much of the resulting conflict has focussed on struggles over water, in its various forms and attributes. It has also played itself out against the backdrop of an immense transformation in the human and physical landscape of postwar Japan. Large-scale migration to cities has been accompanied by almost total urbanization and industrialization of coastal areas. At the same time, rivers and their banks, as well as over half the country’s coast, has been cast in concrete, with consequences that are only now being acknowledged. Dams were built across nearly all of Japan’s rivers to provide power for industry, as well as water for the cities and irrigation for farmers. The combination of steep and thickly wooded mountain slopes and packed but productive plains, consisting largely of paddy fields, combined to form a potent protection force against flooding, but with urbanization in the flood plains and widespread reforestation to conifers in the mountains, the land lost its absorptive capacity. Japan’s rivers flood easily; they are generally quite short, rushing down narrow valleys before wandering sluggishly through alluvial flood plains, where in the summer months, swollen by seasonal rains, they are liable to burst their banks. All told, the presence of water is as remarkable a feature of the Japanese landscape as is the presence of mountains. Equally remarkable, however, is the aesthetic impoverishment of the landscape resulting from the encasement of rivers. The dramatically manipulated landscape of rivers, their beds, banks and flood plains serves as a setting to the issues that are examined in this paper. These concern civil society, especially in the context of a rise in volunteer activity in recent years, a growth that both coincided with and was spurred

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: none
Teacher disagreement score0.632
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Citations20
Published2005
Admission routes1
Has abstractyes

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