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Record W2074853006 · doi:10.1080/00221341.2012.682227

An Assessment Instrument to Measure Geospatial Thinking Expertise

2012· article· en· W2074853006 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Geography · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGeospatial analysisMeasure (data warehouse)Context (archaeology)Test (biology)Benchmark (surveying)Critical thinkingComputer scienceData scienceMathematics educationKnowledge managementPsychologyGeographyCartographyData mining

Abstract

fetched live from OpenAlex

Spatial thinking is fundamental to the practice and theory of geography, however there are few valid and reliable assessment methods in geography to measure student performance in spatial thinking. This article presents the development and evaluation of a geospatial thinking assessment instrument to measure participant understanding of spatial relations within a geographic context. The instrument is a test consisting of thirty question items, with the purpose to help identify the knowledge sets, thinking skills, and characteristics typical at different levels of competency. The performance score is used to classify participants along an expertise continuum, novice to expert. Generally, students performing at the expert level consist of graduate students while novices are at the grade nine level, although several anomalies are discussed. The broad instructional application of this assessment is to benchmark student performance and the level of understanding of geospatial concepts.

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.

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)0.002
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 score0.110
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.397
Teacher spread0.356 · 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