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Record W2530080224

Tourism and the Canadian National Parks System: Protection, Use and Balance

2009· book-chapter· en· W2530080224 on OpenAlexaboutno aff
Stephen Boyd, Richard Butler

Bibliographic record

VenueRoutledge eBooks · 2009
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsTourismRecreationGeographyNational parkLegislationDominance (genetics)Political scienceNarrativeEnvironmental planningEnvironmental protectionEnvironmental resource managementArchaeologyLaw
DOInot available

Abstract

fetched live from OpenAlex

This was a requested chapter by the editors. The book examined the history and development of national park systems from around the world. This chapter provides the narrative of the development of one of the earliest park systems that took place in what was called 'New World' countries. The story of national parks in Canada is one of accident, followed by an ad hoc strategy until the early legislation of Park Acts were in place. Thereafter the development of the system expanded from a dominantly western one centred around the Rocky mountains to cover almost all regions of Canada from the Atlantic coast, through Ontario, the central prairies, to the west coast and expanding into the northern periphery. The narrative of development is one of balancing the dual mandates of protection and use, recognising that while the system is in place to ensure ecological integrity of unique ecosystems, the dominance of recreation and in particular tourism have become the major drivers of the system and how it is perceived. The chapter addresses the challenges of managing spaces where both mandates have to be upheld, something even more challenging for parks established in the far north where First Peoples and their views and traditions have also to be taken into this mix.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.051
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.003
Scholarly communication0.0050.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.011
GPT teacher head0.167
Teacher spread0.156 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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Same venueRoutledge eBooksSame topicAmerican Environmental and Regional HistoryFrench-language works237,207