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Record W7015361613

Social Sustainability in Crowded Parks: Carrying Capacity for People with Mobility-Related Disabilities

2022· article· en· W7015361613 on OpenAlexaboutno aff

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2022
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodTSG101DiafiltrationHyporeflexiaLiquationDysgeusiaArticular cartilage damage
DOInot available

Abstract

fetched live from OpenAlex

As one of the largest minority groups in the world, people with disabilities are largely underrepresented in conversations regarding tourism policy. A large proportion of this demographic have mobility-related physical disabilities (PwMDs), therefore highlighting the importance of including PwMDs in discussions of visitor experience. Important forms of tourism in Canada, specifically in British Columbia (BC), include provincial and national parks. In the recent age of park crowding and over visitation, crowded BC parks have become a widespread concern. Although measurements of social carrying capacity attempt to quantify at what point visitor experience diminishes, there has been no focus on the perspectives of PwMDs. Using in-situ, semi-structured interviews in parks on Vancouver Island, BC, this study will satisfy a critical academic gap within topics of tourism crowding and capacity. Interview response themes will suggest solutions for park management and encourage future research that investigates topics concerning quality park experiences for PwMDs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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