MétaCan
Menu
Back to cohort
Record W4254157966 · doi:10.18356/593adf38-en

Legal, policy and institutional framework

2019· book-chapter· en· W4254157966 on OpenAlexaboutno aff

Bibliographic record

VenueGeneva timber and forest study papers · 2019
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Central asiaSection (typography)Political scienceGeographyEconomyBusinessEconomicsInternational tradeArchaeology

Abstract

fetched live from OpenAlex

Laws, policies and institutions are the main tools used by societies to achieve their objectives, in this case, sustainable forest management. This section describes the laws, policies and institutions relevant to forests and forest management which are in place in the Caucasus and Central Asia. The starting point was the same for all eight countries, the system in place in the USSR in the 1980s, characterised by public ownership of all forest land, and strong central direction from Moscow, through directives, financialsupport, and a clear division of labour between the various parts of the USSR. In Caucasus and Central Asia, priority was given to the protection functions, as practically all forests were classified as protective (Group 1). Other parts of the USSR were charged with wood production. Some features of this approach may be detected today, despite the profound social and economic changes over the past quarter century.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0060.016
Scholarly communication0.0160.007
Open science0.0020.004
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0150.006

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueGeneva timber and forest study papersSame topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207