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Record W7020975929

Mountains on Fire: Making Sense of Change in Waterton Lakes National Park

2021· dissertation· en· W7020975929 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsnot available
Fundersnot available
KeywordsNational parkResource (disambiguation)Sense of placeHappinessRock artRestoration ecologyPhoto elicitation
DOInot available

Abstract

fetched live from OpenAlex

In 2017 the Kenow wildfire burned thirty-eight percent of Waterton Lakes National Park (WLNP) in southern Alberta at high to very high severity in mere hours. The ecological impacts of the fire will have implications for resource management, including the practice of ecological restoration, for decades to come. 
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\nIn this thesis I ask two main questions. First, in what ways are people who are involved in managing WLNP’s ecosystems experiencing the effects of the Kenow wildfire, and how does their experience combined with the severity and extent of the Kenow wildfire influence park management and ecological restoration approaches in WLNP? Subsidiary to this, I ask, what is the role of history, and the role of future climate projections in managing the post-fire landscape? 
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\nThis research is part of the larger Mountain Legacy Project (MLP), which is systematically repeating historic survey photographs taken in the early 1900s across Canada’s mountain landscapes. I use third-view photographs in photo-elicited semi-structured interviews with park staff to answer my first question. In my second research question I ask what broader themes and specific issues do third-view repeat mountain photographs elicit about ecological restoration and park management. As a follow up, I inquire into what ways photo-elicitation functions as an effective method in park management research? Fourteen participants were interviewed, the majority were resource conservation staff, in addition to one retired park warden, a member of the cultural resources unit, a communications staff, and a former staff member. 
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\nParticipants felt wide-ranging emotions relating to the Kenow fire including grief over loss, happiness about regrowth, excitement about learning, anxiety about people’s safety, and stress over increased workloads. Park management frames vegetation regeneration after the Kenow fire as renewal, accepting that the landscape may look different than it did before the fire. Climate change is only beginning to be integrated into ecological restoration, though park management is adapting to climate change by encouraging renewal under a new climate. Historical knowledge still guides decision making in several ways. Major restoration projects including invasive species management, whitebark and limber pine restoration, and prescribed burning, were all impacted by the Kenow fire. Participants shared their thoughts on unconventional approaches such as novel ecosystems, highlighting misunderstandings and misapprehensions about the concept. Parks Canada has an opportunity to learn from Waterton Lakes’ experience to help streamline their post-emergency response in the future. 
\nFindings relating to my second question show these themes and issues were discussed most often by participants when looking at the third-view mountain photographs: fire behaviour, regeneration/renewal, and ecological impacts of the Kenow fire; encroachment; prescribed burning; personal narratives; ecological effects of climate change; and other snapshots. Just less than half the participants did not engage significantly with the photos, which highlights a challenge in using researcher chosen photos. However, many participants did engage and had much to say about the photos, including sharing memories and personal stories. Pre-determined interview questions were essential in unearthing the findings in this thesis, as the photos did not elicit this information alone.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.885

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.313
Teacher spread0.251 · 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
Published2021
Admission routes1
Has abstractyes

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