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Record W2997482721 · doi:10.1007/s10708-019-10128-9

Iceland as a therapeutic landscape: white wilderness spaces for well-being

2020· article· en· W2997482721 on OpenAlexaff
Kaelan Brooke, Allison Williams

Bibliographic record

VenueGeoJournal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWildernessHuman geographyWhite (mutation)GeographyNatural (archaeology)Field (mathematics)AestheticsEnvironmental ethicsArchaeologyEcologyArtEconomic geography

Abstract

fetched live from OpenAlex

Abstract Therapeutic landscapes are reputed to have a lasting repute for realizing healing. Traditional therapeutic landscapes have recognized natural environments as often sought after places for well-being. Such places promote wellness via their close encounter with nature, facilitating relaxation and restoration, and enhancing a combination of physical, mental, and spiritual healing. The physical environment of Iceland is explored through a case study approach, primarily employing data from the field notebooks of post-secondary students travelling in Iceland, as well as the authors’ ethnographic field experience in Iceland. Iceland is examined using both a traditional understanding of therapeutic landscapes, as well as the contemporary understanding of the coloured landscape. In addition to the colour white, reflected in the glacial ice, moving water, and geo-thermal steams, black and various other colours in combination are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.008
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.248
Teacher spread0.232 · 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 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

Citations39
Published2020
Admission routes1
Has abstractyes

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