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Record W3154473680 · doi:10.1111/rssa.12698

John Haigh 1941–2021

2021· article· en· W3154473680 on OpenAlexaboutno aff
Charles M. Goldie

Bibliographic record

VenueJournal of the Royal Statistical Society Series A (Statistics in Society) · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipClassicsGrammar schoolLawSociologyHistoryArt historyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

John Haigh, who died on 9 March 2021 aged 79, was the pre-eminent populariser of probability for his time.Through many publications and media appearances, he perfected the rare art of explaining subtle concepts and calculations to those who would claim no mathematical knowledge.Though his audiences may not have realised it, his standards were high; he never shielded them from a calculation they could do or a concept they could master.He recounted with delight how a colleague had reported her stockbroker husband saying to her at midnight 'I can't come to bed just yet dear: I have to finish one of the calculations in Dr Haigh's book'.John was born on 31 December 1941 and grew up as an only child in Skelmanthorpe, a village near Huddersfield in Yorkshire, where his father worked in the woollen mill and his mother in the canteen.His Methodist upbringing gave John a valuable objectivity in later life as an expert on gambling: gambling was frowned upon, but winning through gambling was especially deprecated.From grammar school ('three hours homework every night'), he won a State Scholarship to read mathematics at Brasenose College Oxford, gaining first-class honours and a University Prize in 1963.Soccer provided a social as well as sporting escape from the general rugby-playing ex-public-school milieu, and John rose to win a Blue, playing for his university against Cambridge at Wembley (where Oxford lost 5-2).Students then were not supposed to gain both a Blue and a First, as the sporty and scholarly subpopulations of undergraduates were largely distinct.D. G. Kendall left Oxford during John's undergraduate years to be the first holder of the Chair of Mathematical Statistics at Cambridge, and his reputation was such that it was no surprise for bright students to follow him to the other place and join the re-invigorated Statistical Laboratory.With a government grant, John started as one of DGK's research students in 1963, enrolling at Gonville and Caius College.He was unlucky in the existence of a link between Caius and his Oxford College, as without that he would have joined Kendall's college, the recently founded Churchill, which looked after its many graduate students, whereas the old colleges neglected them.The young Mr Kingman, as Sir John Kingman then was, took over some of Kendall's research students in 1964.John Haigh was among them, and when Kingman left for the University of Sussex, it was natural for him to follow, though remaining registered for a Cambridge PhD.From his research studentship, John progressed to a lectureship at Sussex, where he stayed for the rest of his career, though spending summers in the 1970s at Melbourne and Stanford, and the year 1983-1984 at the University of Guelph in Canada.At Sussex, he was promoted to Senior Lecturer in 1989 and to Reader in 1993.Retirement was a progress through many stages, and John was still giving a lecture course to first-year undergraduates in his 70s.John's thesis was on random equivalence relations, a combinatorial topic but with a biological motivation; early papers were thus on applications of probability to questions in biology and genetics.Collaboration with the biologist John Maynard Smith led to five important joint papers, laying down the mathematical theory of concepts such as evolutionarily stable strategy, which underpin much of later thinking about evolution.Subsequent work, mostly single-authored but interspersed with joint papers with various co-authors, established John as an expert on combinatorial applied probability,

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.088
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0880.040

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 head0.050
GPT teacher head0.354
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of the Royal Statistical Society Series A (Statistics in Society)Same topicForecasting Techniques and ApplicationsFrench-language works237,207