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Record W2116931011 · doi:10.1139/x08-015

Comanaging communication crises and opportunities between Northern Secwepemc First Nations and the province of British Columbia

2008· article· en· W2116931011 on OpenAlexaffvenueabout
Garth Greskiw, John L. Innes

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsWestern Forest Products
Fundersnot available
KeywordsStaffingActive listeningLiteracyReading (process)Public relationsPolitical scienceSustainable forest managementNatural resourceSustainable managementNatural (archaeology)Environmental resource managementGeographyForest managementSociologyEcologySustainabilityForestryEconomics

Abstract

fetched live from OpenAlex

The Northern Secwepemc First Nations of central British Columbia are facing serious communication challenges in relation to the comanagement of natural resources in their traditional territories. For First Nations’ managers, communication by speaking and listening and by sharing stories continues to be important for maintaining traditional ecological knowledge and culture. However, in the dominant discourse currently used by management authorities, emphasis is placed on communication products represented in reading and writing, often in electronic format. This dichotomy is leading to communication crises, with traditional ecological knowledge being required to fit within a rigid technology of literacy. The hypothesis that the Northern Secwepemc First Nations are leading transformation initiatives toward sustainable management in their territories and that shared knowledge and responsibility emerges from new growth opportunities in crisis situations has been tested using the case study survey method for inquiry. Results indicate there is potential for transformation towards forest comanagement in Northern Secwepemc territories in times of crises; however, certain conditions such as adequate staffing, funding, and training must first exist at the site level of management for both provincial and Aboriginal managers, to make the best use of emergent opportunities for collaboration.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.005
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.159
GPT teacher head0.379
Teacher spread0.220 · 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 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

Citations11
Published2008
Admission routes3
Has abstractyes

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