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Record W4417490561 · doi:10.1659/mrd.2025.00015

Institutional Barriers to Climate Change Adaptation for Mountain Guides in the Canadian Rockies

2025· article· en· W4417490561 on OpenAlexaffabout
Katherine Hanly, Graham McDowell

Bibliographic record

VenueMountain Research and Development · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAdaptabilityCharterMandateAdaptation (eye)Climate changeUnintended consequencesEquity (law)National park

Abstract

fetched live from OpenAlex

Recent revisions by Parks Canada to the National Park Guided Business Licence (NPGBL) program were, in part, a response to challenges that threatened the organization's Charter and Mandate (eg high visitation and congestion). However, findings from this study demonstrate that the revisions have also yielded unintended consequences for mountain guides, including those working in the rapidly changing Canadian Rockies. Using a mixed-methods approach that combines semistructured interviews (n = 30), policy analysis, and key informant interviews (n = 7), we analyzed the origins of revisions to the NPGBL that impact adaptive capacity, their effects on mountain guides' adaptation actions, and potential strategies to overcome these institutional barriers. Findings revealed that two thirds of guides interviewed (67%, n = 20) believe the NPGBL's revised regulatory framework, application process, and implementation mechanisms have inadvertently reduced guides' adaptability while increasing the administrative burden of operating in national parks. Our results suggest that this has reduced the efficacy, efficiency, and equity of adaptation efforts among guides. We found that these barriers can be addressed by fostering horizontal and vertical social capital, which can facilitate the inclusion of local knowledge holders, such as mountain guides, in decision-making processes to yield mutually beneficial outcomes. These findings offer relevant insights for land managers and guiding organizations in Canada and other mountain regions globally where guiding professions are increasingly impacted by the dual pressures of climatic and institutional changes.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.006
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.350
Teacher spread0.264 · 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 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
Published2025
Admission routes2
Has abstractyes

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