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Geography Education: Fieldwork and Contemporary Pedagogy

2018· other· en· W2900302835 on OpenAlexaff
Terence Day, Rachel Spronken‐Smith

Bibliographic record

VenueInternational Encyclopedia of Geography · 2018
Typeother
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsOkanagan College
Fundersnot available
KeywordsFormative assessmentExperiential learningEquity (law)PedagogyWork (physics)SociologyMathematics educationPsychologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

Geographers have taken a leading role in post‐secondary pedagogic innovation, and this is reflected in a rapidly evolving landscape of teaching and learning within the discipline. Lectures used to involve the transmission of information from the lecturer to the student but are now much more interactive, with multimedia, student discussions, interactive exercises, and formative assessment based on “clickers.” Practical and laboratory work is now much more relevant than formerly, with the use of real datasets and less emphasis on repetitive calculations. Fieldwork is still important and safety issues are taken seriously, but equity, risk, and legal liabilities have discouraged some departments and instructors. In contrast, signature pedagogies, including experiential and service learning, along with undergraduate research and inquiry, have seen growth. Textbooks are moving into electronic formats that allow them to be more closely integrated into active learning.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0070.026
Scholarly communication0.0120.008
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0210.003

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 head0.015
GPT teacher head0.340
Teacher spread0.326 · 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 designTheoretical or conceptual
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

Citations8
Published2018
Admission routes1
Has abstractyes

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