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Record W2787309829 · doi:10.3233/sji-180424

The transformation of IAOS 1985–2015: Learning from the past

2018· article· en· W2787309829 on OpenAlexaff
Stephen Penneck

Bibliographic record

VenueStatistical Journal of the IAOS · 2018
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsSt. Stephen's University
Fundersnot available
KeywordsTransformation (genetics)Chemistry

Abstract

fetched live from OpenAlex

The paper provides a summary of the history of the IAOS from 1985 to 2017. Using archive material and recollections of former Presidents it charts the beginnings of the Association in the mid-1980s, reminding us of its original purpose. The paper discusses the successes of setting up the Journal, the Young Statisticians Prize and the web site. It covers the Malaguerra report which reviewed its purpose in 1999 as well as the ‘revitalising’ discussions of 2003. The paper gives useful tables of IAOS conferences, presidents and membership figures, and discusses some of the challenges it has faced, some of which remain relevant today.

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)0.017
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.011
Science and technology studies0.0030.006
Scholarly communication0.0100.005
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.081
GPT teacher head0.424
Teacher spread0.343 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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Same venueStatistical Journal of the IAOSSame topicHealth and Medical Research ImpactsFrench-language works237,207