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Record W4399515041 · doi:10.1111/insr.12576

A Conversation With Marc Hallin

2024· article· en· W4399515041 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Statistical Review · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMedalLibrary scienceClassicsHistoryArt historyComputer science

Abstract

fetched live from OpenAlex

Summary Marc Hallin was born in Ghent, Belgium, on 23 April 1949. He holds a Licence en Sciences mathématiques (1971), a Licence en Sciences actuarielles (1972), and a Doctorat en Sciences (1976) from the Université libre de Bruxelles . He then rose through the professorial ranks at the same institution, being successively Premier Assistant (1977–1978), Chargé de Cours associé (1978–1984), Chargé de Cours (1984–1988), Professeur ordinaire (1988–2009), and Professeur ordinaire émérite upon retirement in 2009. Throughout his career, he supervised 25 PhD students and held invited positions at many institutions of high standing in Austria, Belgium, England, France, Hong Kong, Italy, Portugal, Spain, Switzerland, and the USA (most notably Princeton). A renown expert in time series analysis, econometrics, and non‐parametric inference, Marc is the author or coauthor of over 250 research papers, for which he received numerous awards, including the Medal of the Faculty of Mathematics and Physics of Charles University in Prague (2006), a Humboldt Forschungspreis from the Alexander von Humboldt Foundation (2012), the Pierre‐Simon de Laplace Award of the Société française de Statistique (2022), and the Gottfried E. Noether Distinguished Scholar Award of the American Statistical Association (2022). He gave several distinguished lecture series, including the 2017 Hermann Otto Hirschfeld Lecture Series at the Humboldt Universität zu Berlin , and the 2018 Mahalanobis Memorial Lecture at the Indian Statistical Institute. Over the years, he co‐edited a dozen books and proceedings, and served on the editorial boards of several journals, including the Journal of Time Series Analysis (1994–2009), the Journal of Econometrics (2013–2019), the Journal of Business and Economic Statistics (2018–), and the Theory and Methods Section of the Journal of the American Statistical Association (2005–). He is a Fellow of the Institute of Mathematical Statistics (1990) and the American Statistical Association (1997), as well as a member of the Classe des Sciences of the Royal Academy of Belgium (1999). Marc has been a member of the International Statistical Institute since 1985 and was (co‐) Editor‐in‐Chief of the International Statistical Review from 2010 to 2015.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0050.002

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 head0.148
GPT teacher head0.485
Teacher spread0.338 · 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