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Record W2136248875 · doi:10.22230/jem.2009v10n2a416

Clayoquot Sound: Lessons in ecosystem-based management implementation from an industry perspective

2009· article· en· W2136248875 on OpenAlexaff
Gordon Butt, Don McMillan

Bibliographic record

VenueJournal of Ecosystems and Management · 2009
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsASL Environmental Sciences (Canada)
Fundersnot available
KeywordsSound (geography)Government (linguistics)Perspective (graphical)Environmental resource managementProcess (computing)BusinessEnvironmental planningPublic relationsPolitical scienceGeographyComputer scienceEconomics

Abstract

fetched live from OpenAlex

In 1995, the Clayoquot Sound Scientific Panel submitted a report with 170 recommendations that fundamentally changed forest management as it had been traditionally practiced in the Sound. The Scientific Panel's report represents an early case study of ecosystem-based management (EBM) implementation. The recommendations were adopted by industry, government, and other participants with hopes that this would end the vociferous conflicts that had come to characterize Clayoquot Sound. British Columbia's Provincial government was committed to working with industry, First Nations, forest workers, and local communities to make the changes happen. However, the implementation was not accomplished easily or cheaply, and it was not an unmitigated success, at least from the perspective of industry. In addition to summarizing the history of the process, this article discusses outcomes of EBM implementation in Clayoquot Sound in terms of planning for environmental values before timber, using ecological rather than administrative boundaries, and engaging participants early and throughout the planning process. With emphasis on implications from an industry perspective, the authors recommend approaches that balance the strengths and challenges inherent in ecosystem-based management in British Columbia.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.895

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.428
Teacher spread0.384 · 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 designQualitative
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

Citations7
Published2009
Admission routes1
Has abstractyes

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