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Record W2059055740 · doi:10.1177/1541931213571273

The Use of Paired Comparisons for Evaluating Complex Route Matching Performance in a Spatial Awareness Task

2013· article· en· W2059055740 on OpenAlexafffund
Anthony Soung Yee, Paul Milgram

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2013
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClosenessTask (project management)Matching (statistics)Identification (biology)Set (abstract data type)Computer scienceContext (archaeology)Paired comparisonStatisticsData setSpatial contextual awarenessArtificial intelligenceData miningPattern recognition (psychology)MathematicsGeographyEngineering

Abstract

fetched live from OpenAlex

A method is proposed for evaluating participants’ performance in a global spatial awareness task involving identification of complex winding routes. Rather than using coarse measures of spatial error, the method of paired comparisons employs impartial judges to compare sets of aggregated experimental data generated by the participants with respect closeness in shape to the target route. The method was applied to a set of data in an experimental investigation of the effect of height on a participant’s ability to identify the route he had just flown over (in a 20 second video). Seven participants performed a total of 48 trials in a 4 (heights) X 2 (trial blocks) within-subjects experiment. Results from the paired comparison analysis suggested that height had a statistically significant effect on correct route identification. Of equal importance in the context of the present paper, the method of paired comparison analysis proved to be effective in quantifying performance data that did not otherwise lend themselves to conventional methods of quantification.

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 score0.453
Threshold uncertainty score0.388

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)0.0000.000

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 head0.070
GPT teacher head0.269
Teacher spread0.199 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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