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Record W2042983512 · doi:10.5539/ass.v7n11p50

The University of the Sea and the Benefits to Student Learning of Participation in a Marine Research Expedition

2011· article· en· W2042983512 on OpenAlexvenueno aff
K.A. Dadd

Bibliographic record

VenueAsian Social Science · 2011
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningLikert scaleMarine researchPsychologyMedical educationPedagogyOceanographyMedicineGeology

Abstract

fetched live from OpenAlex

The University of the Sea has provided university students from the Asia-Pacific region with experience on multi-week, marine research expeditions since 2004. The program is UNESCO-funded and generously supported by Geoscience Australia. During 2007 and 2008, students were surveyed to ascertain whether they felt the program was a valuable learning experience. The survey had both Likert-scaled and open-ended questions. The students enjoyed the experience, found it valuable, appreciated putting theory into practice, and liked the interaction with scientists. They gained skills and knowledge that will help guide their career paths. However, most felt they required more information prior to the expedition, and a greater knowledge of the research aims and their role in the expedition. Students incorrectly assumed the expedition would be tailored to their learning and would provide didactic learning experiences. They did not automatically see the experiential learning activity was valuable in itself.

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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.086
GPT teacher head0.408
Teacher spread0.322 · 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 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
Published2011
Admission routes1
Has abstractyes

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