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Record W2900595697 · doi:10.1139/er-2018-0057

Governance as a driver of change in the Canadian boreal zone<sup>1</sup>

2018· article· en· W2900595697 on OpenAlexafffundvenueabout
Gillian E. Fuss, James W.N. Steenberg, Marian Weber, Mike Smith, Irena F. Creed

Bibliographic record

VenueEnvironmental Reviews · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of SaskatchewanUniversity of British ColumbiaNova Scotia Department of AgricultureLakehead UniversityAlberta InnovatesDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorporate governanceEcosystem servicesDecentralizationMarketizationEnvironmental resource managementGovernment (linguistics)Sustainable forest managementBusinessForest managementPolitical scienceGeographyEcosystemEcologyEconomicsForestry

Abstract

fetched live from OpenAlex

The Canadian boreal forest is primarily public land, owned and managed by provincial governments on behalf of the public interest. Boreal forest governance consists of a complex patchwork of federal and provincial legislation, policies, tenures, and delegated authorities designed to achieve multiple (and often conflicting) social, ecological, and economic objectives. We examine the implications of boreal governance arrangements for sustainable management of ecosystem services. The paper shows how current multi-level governance arrangements that evolved from Canada’s Constitution Act are not effective at managing the cross-scale and cross-sectoral challenges of ecosystem services and have created a crisis of legitimacy for forest decisions. We show how the rise of nonstate arrangements, marketization, and decentralization are partly a response to governance gaps for ecosystem services as well as a reflection of global trends in forest governance. Past trends related to governance themes (the role and scope of government, the level of integration and coordination, Indigenous empowerment, and geopolitical influences) are used to motivate future governance scenarios.

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 score0.552
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.012

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 head0.026
GPT teacher head0.256
Teacher spread0.229 · 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

Citations18
Published2018
Admission routes4
Has abstractyes

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