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Record W2969839779 · doi:10.11575/prism/10110

Bagging Peaks and Busting Trails: Place-Making in the Canadian Rockies

2011· article· en· W2969839779 on OpenAlexaboutno aff
Lauren Harding

Bibliographic record

VenueOpen MIND · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Based on ethnographic research conducted during the summer of 2009, I explore affective narratives of landscape and memory created through the activity of backpacking.Using the town of Banff as my 'base-camp', I hiked the surrounding trails, interviewing other Canadians engaged in both the literal and figurative exploration of wilderness through backcountry camping.Arguably, the activity itself is founded upon a problematic modernist (and colonial) separation between nature and culture, and backpackers continue to reify this separation through their idealization of wilderness.However, because hikers engage in a much more tangible and direct way with the landscape than most tourists, they are often simultaneously confronted with places, moments, and actions that do not fit clearly into the paradigm of a pristine wild space.By traversing the landscape, hikers have a sensory encounter with place that possesses far more depth than the panoramas of a typical tourist photograph.Blood, sweat and tears are often shed on these trips, and encounters with snow, hail, rockslides, and wildlife transform the experience.Hiking thus acts as a more complex form of 'place-making' than usually discussed in critiques of tourism, and yields insights into the ways in which Canadians imagine and engage with their 'wild backyard'.Banff's mystique relies on its characterization as a space empty of society, as a pristine wilderness, and yet it is also layered with social signification as a place: home, recreation ground, ancestral territory.Geographers, historians, and anthropologists from William Cronon(1995) to

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.172
GPT teacher head0.372
Teacher spread0.200 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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