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Record W2609406768 · doi:10.13140/rg.2.2.15976.67843

Gender, participation, and environmental decision-making: Case study of the proposed Jumbo Glacier Resort

2017· article· en· W2609406768 on OpenAlexaboutno aff
Rayne Tarasiuk

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierEnvironmental justiceEnvironmental planningEnvironmental resource managementGeographyPolitical scienceEnvironmental sciencePhysical geography

Abstract

fetched live from OpenAlex

This study focused on understanding the role of gender in people’s participation in the proposed Jumbo Glacier Resort decision-making processes in southeastern British Columbia, Canada. Research questions focused on the ability of Transgender people, women, and men to participate meaningfully, and the identification of enabling and constraining factors of participation. Respondents had experience with formal and/or informal decision-making processes regarding the proposed Jumbo Glacier Resort. Qualitative methodology was used to gather data through interviews, surveys, site research, and archival sources, which were analyzed using thematic content analysis. According to most respondents, gender impacted people’s participation in the proposed Jumbo Glacier Resort decision-making processes by determining whether they held the right “key” to access participation; which respondents determined was held by men. 
\nConsequently, these findings show that the proposed Jumbo Glacier Resort decision-making processes were gendered, and were not representative of everyone who was impacted by the decisions. In 2017 this type of exclusion is no longer appropriate, and as both the literature and numerous international agreements point to, in order to achieve human sustainability, we all need to work together.

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 score0.101
Threshold uncertainty score0.761

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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