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Record W2320686758 · doi:10.5558/tfc2015-024

Understanding First Nations rights and perspectives on the use of herbicides in forestry: A case study from northeastern Ontario

2015· article· en· W2320686758 on OpenAlexaffvenueabout
Gordon J. Kayahara, Carly L. Armstrong

Bibliographic record

VenueThe Forestry Chronicle · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsOpposition (politics)TreatyFirst nationPolitical scienceHuman rightsEnvironmentalismPublic administrationEnvironmental protectionEnvironmental ethicsLawGeographyEcology

Abstract

fetched live from OpenAlex

This article provides forestry professionals with an improved understanding of why First Nations are opposed to the use of chemical herbicides for silvicultural purposes on their traditional lands, based on a case study in northeastern Ontario. Results were generated using a modified form of a focus group approach. First Nations opposition to herbicide use involved not only concerns over human and environmental health (concerns common among the general public) but also spanned from treaty rights, mistrust, and respect issues to herbicide use being incongruent with traditional First Nations worldviews. The results illustrate that the science-education approach typically used to address public opposition to herbicides is neither adequate nor appropriate for addressing First Nations concerns. Instead, a more in-depth engagement and approach, centred on genuine respect for First Nations rights, culture and history, is needed to arrive at solutions that are consistent with each First Nation community's values and terms.

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 score0.410
Threshold uncertainty score0.826

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 science0.0000.000
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 head0.351
GPT teacher head0.247
Teacher spread0.104 · 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

Citations13
Published2015
Admission routes3
Has abstractyes

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