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Record W4408574967 · doi:10.18060/28737

Statistics For Librarians: Probability and Odds

2025· article· en· W4408574967 on OpenAlexaff
Caitlin Bakker

Bibliographic record

VenueHypothesis Research Journal for Health Information Professionals · 2025
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsOddsStatisticsProbability and statisticsComputer scienceMathematicsLogistic regression

Abstract

fetched live from OpenAlex

In this column, theoretical and empirical probability and odds are discussed, including assumptions and calculations. Independent and dependent events, including mutually exclusive events, are covered.

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)0.015
metaresearch head score (Gemma)0.226
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: Methods · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.226
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.016
Science and technology studies0.0020.006
Scholarly communication0.0100.012
Open science0.0010.003
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0430.027

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 head0.563
GPT teacher head0.603
Teacher spread0.040 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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