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Record W2155484965 · doi:10.1002/cjs.11259

The joy of proofs in statistical research

2015· article· en· W2155484965 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Statistics · 2015
Typearticle
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematical proofPreambleMedalStatistical analysisComputer scienceMathematicsStatisticsHistoryArchaeology

Abstract

fetched live from OpenAlex

Abstract Without exception, great statisticians have penetrating statistical insight. Technical strength is likely secondary in their research, although it never hurts. In contrast, mathematical skill has been pivotal in my work, particularly in combination with my proximity to many great statistical minds. In the occasion of receiving the Gold Medal of the Statistical Society of Canada, I wish to attribute part of my success to the joy of proofs. Proofs are an indispensable ingredient in my research achievements. I hasten to add that proofs are delightful only if they help to develop innovative statistical methods: proving theorems solely for the sake of publication can be boring and a waste of time. With this preamble, I selectively present some research achievements and encourage other researchers to proudly maximize the benefits of their technical strength. The Canadian Journal of Statistics 43: 481–497; 2015 © 2015 Statistical Society of Canada

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)0.005
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.179
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.441
GPT teacher head0.510
Teacher spread0.069 · 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