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Record W2903878400 · doi:10.1080/03098265.2018.1554632

Student field experiences: designing for different instructors and variable weather

2018· article· en· W2903878400 on OpenAlexaff
Alison Jolley, Samuel J. Hampton, Erik Brogt, Ben Kennedy, Lyndon Fraser, Angus Knox

Bibliographic record

VenueJournal of Geography in Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsUniversity of British Columbia
FundersUniversity of Canterbury
KeywordsField (mathematics)Variable (mathematics)Mathematics educationEnvironmental sciencePsychologyMeteorologyGeographyMathematics

Abstract

fetched live from OpenAlex

This study compares the field experience and development of sense of place (in this case, human attributed meanings and attachments to the field area) in geoscience students on three separate course sections of a six-day introductory geological mapping field trip. Students stayed in a small farm station within their 4 km2 field area, worked in groups of three or four, and produced an individual final assessment. Findings from student interviews and pre-post surveys indicated that there were no significant differences in perceptions of the field trip purpose or sense of place between field trip sections, despite differences in instructor pedagogy and sense of place, as well as varied weather conditions. There were significant increases in student sense of place on all field trips, in contrast with previous work on a “roadside” (regional, multi-site) field trip where no significant change in sense of place occurred. In-field observations and instructor interviews identified key characteristics that supported similar sense of place and experiences on all trips: (1) consistent intended learning outcomes, (2) a carefully selected and immersive field area valued by instructors, and (3) an assessment connected to the landscape/field area with flexibility in its implementation, especially when faced with adverse weather conditions.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.373
Teacher spread0.337 · 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 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

Citations22
Published2018
Admission routes1
Has abstractyes

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