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Record W7041632273

The Last Actuary

2020· article· en· W7041632273 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

VenueResearch Portal (Queen's University Belfast) · 2020
Typearticle
Languageen
FieldMathematics
TopicProbability and Statistical Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsActuaryCompetition (biology)Product (mathematics)Big dataNew product developmentBusiness model
DOInot available

Abstract

fetched live from OpenAlex

The essays in this competition were asked to explore several aspects of how technology transformations are likely to impact actuarial practice innovation in the future, including:<br/><br/>• How actuaries have designed innovative solutions using more advanced approaches than in the past<br/>• Collaborative efforts where actuaries have championed innovation across a wide array of professions<br/>• Using new sources of big data to drive product development and bring new products to market<br/>• Designing more dynamic models that can readily be adjusted as new information becomes available<br/><br/>A panel of judges reviewed the essays for publication and awards. The judges selected three essays for awards, one for an honorable mention and a further three for publication. Consideration was given to creativity, relevance, and economic and business impact.<br/><br/>Article available here: https://proactuary.com/the-last-actuary/

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.349
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