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Record W2507461084 · doi:10.1093/forestry/cpw034

Aboriginal people and forestry companies in Canada: possibilities and pitfalls of an informal ‘social licence’ in a contested environment: Table 1

2016· article· en· W2507461084 on OpenAlexaboutno aff
Stephen Wyatt

Bibliographic record

VenueForestry An International Journal of Forest Research · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationFraming (construction)Table (database)Public relationsBusinessPolitical scienceLeverage (statistics)Community forestryForestryForest managementEngineeringGeographyComputer scienceLaw

Abstract

fetched live from OpenAlex

Industrial forestry in Canada commonly occurs on the traditional territory of Aboriginal people, and forestry companies often take actions to gain acceptance and approval of communities. Over the last two decades, the concept of ‘social licence to operate’ (SLO) has been increasingly used as a way of framing these actions in relation to regulatory licences and approval processes. Although Aboriginal views of forestry have been extensively researched, few attempts have been made to link this research to the developing concept of SLO. This article seeks to address this gap, using existing models to identify five groups of path elements that contribute to obtaining and maintaining SLO: socio-economic infrastructure, biophysical infrastructure, engagement processes, relationship building and recognition of rights. Previous research on five common forms of collaborative arrangement – impact benefit agreements, co-management, consultation processes, tenures and economic partnerships – is then reviewed to consider how these contribute to obtaining and maintain SLO. Canadian experiences demonstrate the potential benefits of direct negotiations and the advantages of combining arrangements, but also highlight the difficulty of addressing Aboriginal rights within an SLO framework.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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.

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

Citations19
Published2016
Admission routes1
Has abstractyes

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