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Record W2098762464

Less Parametric Methods in Statistics

2005· article· en· W2098762464 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

VenueRepository of the University of Ljubljana (University of Ljubljana) · 2005
Typearticle
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMetric (unit)Parametric statisticsContext (archaeology)EstimationEconometricsComputer scienceParametric modelStatistical analysisRevelationStatisticsMathematicsOperations researchData scienceSociologyEconomicsHistoryManagementOperations managementPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Despite forty years of revolution in the tools available for statistical analysis, the current academic tradition in statistics is remarkably similar to the pre-computer tradition. This tradition is rooted in parametric modeling, estimation, and testing, and optimal procedures based on these models. The paper argues for a shift in emphasis away from parametric modeling and estimation to graphical summary, from omnibus optimal techniques to those that are more context-specific, and from goals of objectivity to goals of revelation. It is suggested that the emphasis in statistical education should be rebalanced to reflect certain modern computer-based techniques. 1

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.305
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.101
GPT teacher head0.367
Teacher spread0.266 · 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