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Record W7116873505 · doi:10.55533/2325-5226.1554

Enhancing Youth Learning Outcomes of Travel Programs through Storytelling

2025· article· en· W7116873505 on OpenAlexaboutno aff
Gary D. Ellis, Parisa Paymard, Dottie Goebel, Darlene Locke, William Zanolini, Emily Catalan, Kelley Ranly

Bibliographic record

VenueJournal of Human Sciences and Extension · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeStorytellingPositive Youth DevelopmentYouth engagementProgram evaluationCultural learningEveningStudy abroad

Abstract

fetched live from OpenAlex

We conducted two studies to evaluate strategies that Extension educators may use to enrich youth travel program experiences. Study 1 evaluated the effects of telling stories about attractions before site visits on youth experiences and learning outcomes on site. Seventeen youth in a 4-H program designed to promote cultural understanding visited eight attractions in Costa Rica. The evening before visiting three sites, youth were told a fictional or cross-fictional story about the sites. The stories elicited imaginary travel to the site (narrative transportation). After visiting the sites, the youth reported the extent to which they felt like they were in a story (narrative re-visitation) while on-site. They also reported their anticipated impact of learning experiences on one of the program’s learning outcomes. Narrative transportation significantly increased narrative re-visitation. Both factors significantly increased the anticipated impact on learning outcomes. In Study 2, 35 4-H youth visited 12 agricultural sites in the Western U.S. and Canada. Three experimental conditions were created by an Extension educator: vastness emphasized via educator comments, vastness not emphasized, and baseline. The two treatment conditions produced greater awe than baseline. The relation between awe and anticipated impact on learning outcomes was significant.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.541

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.109
GPT teacher head0.341
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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

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