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Record W2092269694 · doi:10.1139/x07-214

First Nations, forest lands, and “aboriginal forestry” in Canada: from exclusion to comanagement and beyond

2008· article· en· W2092269694 on OpenAlexafffundvenueabout
Stephen Wyatt

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversité de Moncton
FundersUniversity of British Columbia
KeywordsForestryCommunity forestryForest managementVisionPolitical scienceGeographySociology

Abstract

fetched live from OpenAlex

The term “aboriginal forestry” is used increasingly to describe the evolving role of First Nations peoples in Canadian forestry over the last 30 years. This paper reviews a diversity of experiences and identifies issues that have important implications for governments, forest planners, and First Nations: a forestry regime that reflects the interests of governments and industry rather than those of First Nations; variable implementation of aboriginal rights in forestry practice; benefits and problems of economic partnerships; limitations on consultation, traditional knowledge, and comanagement in forestry; and finally, different forestry paradigms. Among these experiences and issues, we recognise different visions for the participation of First Nations peoples in Canadian forestry. At one end of the spectrum, “forestry excluding First Nations” is no longer accepted. The most common form may be “forestry by First Nations,” representing a role for First Nations within existing forestry regimes. Other options include “forestry for First Nations,” in which forest managers seek to incorporate aboriginal values and knowledge in management activities and “forestry with First Nations,” in which aboriginal peoples are equal partners in forest management. However, aboriginal forestry is better understood as a potential new form of forestry that uses knowledge and techniques drawn from both traditions and conventional forestry and is based on aboriginal rights, values, and institutions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.577

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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

Citations116
Published2008
Admission routes4
Has abstractyes

Explore more

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