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Record W4393859788 · doi:10.25071/s8egjr57

Interview: Steve MacBeth, MSC, MSM, CD, Chief Operating Officer, Team Rubicon Canada

2023· article· en· W4393859788 on OpenAlexaboutno aff
Steve MacBeth, Simon Wells

Bibliographic record

VenueCanadian Journal of Emergency Management · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsnot available
Fundersnot available
KeywordsOfficerManagementAeronauticsOperations researchMedicineHistoryEngineeringArchaeology

Abstract

fetched live from OpenAlex

Simon Wells interviewed Steve MacBeth on December 7th, 2022. Here is the transcript of their discussion.

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)0.003
metaresearch head score (Gemma)0.020
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: none
Teacher disagreement score0.547
Threshold uncertainty score0.901

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0550.012

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 head0.185
GPT teacher head0.407
Teacher spread0.222 · 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
Published2023
Admission routes1
Has abstractyes

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