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Record W1966903545 · doi:10.3138/ijcs.49.253

Selling the Scenery or Preserving the Wilderness: Canadian Members of Parliament and their Views on the Purpose of National Parks, 1945–64

2014· article· en· W1966903545 on OpenAlexvenueaboutno aff
Paula Saari

Bibliographic record

VenueInternational Journal of Canadian Studies · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationParliamentNational parkWildernessTourismPolitical scienceHouse of CommonsValue (mathematics)Natural (archaeology)Nature tourismWilderness areaNatural resourceCommonsAmbiguityGeographyPublic administrationEnvironmental ethicsPublic relationsEcotourismLawPoliticsArchaeology

Abstract

fetched live from OpenAlex

This article examines Canadian parliamentary discussion on national parks from 1945 to 1964 and traces how the members of Parliament viewed the purpose of national parks and the value of nature. The article shows how comments in the House of Commons reflected local concerns and the ambiguity Canadians felt about national parks, with opinions shifting between the development of tourism and recreational use and the need to preserve natural areas. Discussion in the House indicated a clear focus on development in the comments of politicians who spoke for constituency interests and the developmental opportunities parks offered politicians’ local areas. While the members were aware of the purpose of parks in preserving areas in their natural condition, they opted to seek economic development through tourism and argued that areas with little tourism potential were not suitable for park purposes. The article concludes that even though the parks were viewed in terms of their development possibilities, during the early 1960s the preservationist sentiment gained more ground. The idea of preservation was still, however, mostly concerned with the preservation of park environments for their recreational use.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.860

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.248
Teacher spread0.207 · 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 designNot applicable
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

Citations1
Published2014
Admission routes2
Has abstractyes

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