MétaCan
Menu
Back to cohort
Record W3081456004 · doi:10.1080/1743873x.2020.1807556

Life beyond growth? Rural depopulation becoming the attraction in Nagoro, Japan’s scarecrow village

2020· article· en· W3081456004 on OpenAlexaff
Atsuko Hashimoto, David J. Telfer, Sakura Telfer

Bibliographic record

VenueJournal of Heritage Tourism · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsBrock University
Fundersnot available
KeywordsContext (archaeology)TourismGovernment (linguistics)Tourist attractionGeographyRural areaEconomic growthResistance (ecology)AttractionSocioeconomicsPolitical scienceSociologyArchaeologyEcology

Abstract

fetched live from OpenAlex

Rural villages in Japan are rapidly ageing and depopulating. On Shikoku Island, in the remote mountainous Iya Valley, is the village of Nagoro. Residents who have left or passed away have been replaced with ‘kakashi’ or scarecrows in the form of life-like dolls. Currently, scarecrows outnumber the village residents and appear throughout the community—waiting at bus stops, working the fields, and studying at the closed school. The attempt to preserve village life and identity through the scarecrow displays has begun to attract the attention of media and tourists. This paper examines this emerging rural tourism attraction in the context of Japanese rural depopulation and peripheralisation. Nagoro is representative of many Japanese villages where the rural lifestyle is disappearing. It has adopted a unique form of quiet resistance to the ending of an era viewed in the context of museumisation and abandoned landscapes. Rural communities are transitioning finding themselves between government rejuvenation policies and learning to live beyond growth.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Heritage TourismSame topicRural development and sustainabilityFrench-language works237,207