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Record W1992185864 · doi:10.5558/tfc85849-6

Recent global trends in forest tenures

2009· article· en· W1992185864 on OpenAlexaffvenueabout
Shashi Kant

Bibliographic record

VenueThe Forestry Chronicle · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Toronto
FundersU.S. Forest Service
KeywordsCorporatizationBusinessCommercializationCorporationForest managementState forestDiversity (politics)State (computer science)Market economyEconomicsGeographyForestryFinancePolitical science

Abstract

fetched live from OpenAlex

Wide-ranging changes in forest tenures have occurred globally in recent decades, and the changes in developed countries and transition economies have been dominated by market forces. Market-based forest tenure changes are discussed in 4 categories: (i) forest management by a state-owned company (Sweden); (ii) commercialization, corporatization, and privatization of plantations (New Zealand, South Africa and Australia); (iii) creation of forest enterprises within state forestry agencies (the United Kingdom, Germany, and transition economies); and (iv) changes in forest tenures in economies in transition. The global tenure changes provide no empirical evidence in support of any specific form of tenure. I suggest 9 guiding principles, instead of a specific type of tenure, for forest tenure reform in Canada. Forest reforms should be organized: (i) keeping the future of forestry in perspective; (ii) for multiple attributes of forests; (iii) to provide flexibility, diversity, and adaptiveness; (iv) to foster forest industry competitiveness; (v) for economically optimal timber supply; (vi) to maximize the value of harvested timber; (vii) to recognize and deal with the non-separation of forest management and timber allocation and harvest; (viii) to select an appropriate organizational form, such as state business enterprise, corporation, or state-owned company, based on a SWOT (strengths, weaknesses, opportunities, and threats) analysis; and (ix) to seek inputs from an expert group–without direct stakeholders. Key words: Australia, Canada, commercialization, corporatization, forest enterprise, forest tenure, Germany, New Zealand, privatization, South Africa, Sweden, transition economies, United Kingdom

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.263
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2009
Admission routes3
Has abstractyes

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