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Record W4384297161 · doi:10.46747/cfp.6907511

Tackling complexity through integration

2023· article· en· W4384297161 on OpenAlexvenueaboutno aff
Christie Newton

Bibliographic record

VenueCanadian Family Physician · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicKnowledge Management and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Healthcare systemPopulationPopulation ageingHealth careIMGComputer scienceData scienceMedicineEconomic growthPolitical scienceEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

![Figure][1] Canada’s family medicine crisis is complex. The health care system faces an aging population, increasing rates of chronic conditions, high rates of migration and ethnocultural diversity, and growing fiscal constraints. Health systems are also facing the challenges of the 4 C s

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)0.014
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0120.020
Scholarly communication0.0200.022
Open science0.0040.023
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0350.007

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 head0.194
GPT teacher head0.362
Teacher spread0.168 · 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
GenreCommentary

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
Published2023
Admission routes2
Has abstractyes

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