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American and Canadian National Park Agency Responses to Declining Visitation

2011· article· en· W2603663376 on OpenAlexaffabout
John Shultis, Thomas A. More

Bibliographic record

VenueJournal of Leisure Research · 2011
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsNational parkAgency (philosophy)GeographyPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

AbstractRecent research has suggested a decline in visits to national parks in both the United States and Canada. We analyzed contemporary (2000 to 2009) national policy documents (e.g., annual reports, strategic plans, commission findings) in both countries to assess national park agency reactions to recent changes in visitation patterns. Neither the Parks Canada Agency nor the National Park Service directly mentioned declines in visitation in these high level documents, focusing instead on identifying major external "challenges" related to building visitation. In response to these challenges, both agencies moved to bolster and redefine their educational efforts to reach new audiences, particularly youth and minority/immigrant groups in urban areas. Both agencies also shared four key assumptions, most significantly the belief that decreased visitation will lead to decreased public and political support for parks. They also ignored potential benefits of decreasing use, such as decreased environmental or social impacts. We suggest that the growing focus on increasing use, in tandem with the contemporary neoliberal political environment, leads these national park agencies to systematically emphasize use values while de-emphasizing preservation values. The park agencies are becoming increasingly focused on "re-engaging" with the public to increase political support for the bureaucracies.KEYWORDS: Declining visitationneoliberalismnational parkspreservation versus 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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.255
GPT teacher head0.487
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations37
Published2011
Admission routes2
Has abstractyes

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