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Record W2585225142 · doi:10.1080/01426397.2016.1267335

Contested periurban amenity landscapes: changing waterfront ‘countryside ideals’ in central Canada

2017· article· en· W2585225142 on OpenAlexafffundabout
Nik Luka

Bibliographic record

VenueLandscape Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAmenityUrbanizationNarrativeGeographyContext (archaeology)Rural areaSociologyEnvironmental planningPolitical scienceEcologyArchaeology

Abstract

fetched live from OpenAlex

Periurban second homes have received limited attention in landscape research, but they can offer important insight for landscape histories of urbanisation. This paper focuses on the hundreds of thousands of waterfront ‘cottages’ or ‘chalets’ found in central Canada’s densely-populated Toronto-Montréal urban corridor. A review of scholarly work examines how wide swaths of forest have become periurban amenity landscapes over the last 150 years. An interwoven theoretical narrative centres on the ‘countryside ideal’—an enduring concept linking Anglo-American attitudes about nature and culture with context-specific assemblages of landscape, urban form, and social practice. Finally, a critical discussion highlights how these periurban amenity landscapes have become increasingly contested, taking stock of new clashes between rapid processes of landscape transformation now underway and the broader Anglo-American images, representations, and material cultures expressing what nature is (or ought to be).

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)0.002
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 score0.107
Threshold uncertainty score0.774

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0160.009
Scholarly communication0.0070.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.371
Teacher spread0.312 · 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

Citations9
Published2017
Admission routes3
Has abstractyes

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