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Record W2951189947 · doi:10.1136/ebnurs-2019-103145

Triangulation in research, with examples

2019· editorial· en· W2951189947 on OpenAlexaff
Helen Noble, Roberta Heale

Bibliographic record

VenueEvidence-Based Nursing · 2019
Typeeditorial
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTriangulationComputer scienceMathematicsGeometry

Abstract

fetched live from OpenAlex

TEST 02 - Elsevier's Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

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)0.154
metaresearch head score (Gemma)0.390
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.813

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1540.390
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.009
Science and technology studies0.0070.026
Scholarly communication0.0150.021
Open science0.0050.013
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0130.003

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 head0.839
GPT teacher head0.730
Teacher spread0.109 · 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.

Study designNot applicable
DomainMethods
GenreEditorial

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

Citations690
Published2019
Admission routes1
Has abstractyes

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