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Record W4206274846 · doi:10.30909/vol.05.01.1132

Teaching about volcanoes

2022· article· en· W4206274846 on OpenAlexfundno aff
Alison Jolley, Jacqueline Dohaney, Ben Kennedy

Bibliographic record

VenueVolcanica · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsVolcanologyFaculty developmentResource (disambiguation)Authentic learningMedical educationEarth scienceProfessional developmentMathematics educationPsychologyComputer scienceMedicineVolcanoGeology

Abstract

fetched live from OpenAlex

Volcanology education is important for the development of geoscientists and scientifically literate citizens. We surveyed 55 volcanology instructors to determine their learning and teaching practices, perceptions of academic development, and educational support needs. Instructors reported using a wide range of practices and tools, but lectures, field experiences, maps, rock samples, academic literature, and inherited teaching materials are the most common. Instructors valued educational support from others (e.g., talking with colleagues and students, consulting with learning and teaching specialists) over conducting their own investigations. However, they did not report engaging in as many of these activities as they valued. Instructors requested more support in resource sharing and collation, conference workshops, and co-creation of resources and educational research. We suggest that instructors and academic development staff work together to share and build knowledge in the learning and teaching of volcanology in higher education, and to improve student learning outcomes.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.026
GPT teacher head0.342
Teacher spread0.316 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2022
Admission routes1
Has abstractyes

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