MétaCan
Menu
Back to cohort
Record W7099236855

1 CHARACTERISTICS OF THE OLDER CAMPER

2016· article· en· W7099236855 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsOlder peoplePopulationElderly peopleAge groupsFalling (accident)Low income
DOInot available

Abstract

fetched live from OpenAlex

People over age 45 are much less likely to camp than people under age 45. As the population of Canada ages there will be a considerable challenge to maintain camping participation. Addressing this issue, this study compared older and younger campers in their attitudes, behaviours and activities relating to camping. It involved an analysis of the 1996 Ontario Parks Camper Survey, using age as the independent variable. The creation of Ontario Parks in 1996 made income goals important for each park. Increasing income through increased visitation (i.e., older campers) could provide each park with a larger budget to carry out its mandate. Many significant social and demographic differences were found between younger and older campers. Older campers are an attractive and lucrative market for parks; however, the existing programs and services are attractive to only a small percentage of people over age 45. The growing need to understand the characteristics of older campers is twofold: to attract an increased number of older people to camping and to decrease the age-related dropout

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.276
Teacher spread0.262 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicRecreation, Leisure, Wilderness ManagementFrench-language works237,207