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Domestic timber auctions and flexibly specialized forestry in Japan

2006· article· en· W2167253954 on OpenAlexafffundvenue
Tim Reiffenstein, Roger Hayter

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsSimon Fraser UniversityMount Allison University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommon value auctionBiddingContext (archaeology)BusinessProduction (economics)Transaction costIndustrial organizationEconomicsEconomyMicroeconomicsGeographyMarketing

Abstract

fetched live from OpenAlex

In Japan, a well‐established, widespread system of local timber market auctions, featuring the exchange of privately owned logs, is increasingly threatened by imports organized according to mass production principles. This article assesses the evolution, rationale, and functions of Japan's timber auctions that were primarily created in post‐war Japan to provide key roles linking small‐scale (private) forest owners to flexibly specialized value chains that are consummated in Japanese homes. The conceptual point of departure for the analysis is flexible specialization theory's interpretation of industrialization as a contest between mass production and small‐scale production. We extend this discussion by giving analytical priority to markets as an institution distinct from firms and by interpreting markets from the perspectives of transaction costs and embeddedness, concepts normally deemed antagonistic to one another. Empirically, four case studies of timber auctions located in central and southern Japan are analyzed based on personal interviews with auction managers and participants within the context of broader trends in forestry. Three auctions feature ‘silent’ bidding and one involves open bidding. While the auctions exhibit varying characteristics, they continue to be the fulcrum of localized forestry systems, even as they are threatened by declining prices driven by imported wood and by restructurings within the Japanese solid wood sector. The continued resiliency of the flexible specialization model, and the auctions that are at its core, has important implications for forestry throughout Japan .

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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.

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

Citations8
Published2006
Admission routes3
Has abstractyes

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