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Record W4220931242 · doi:10.15353/acmla.n170.4873

OpenRefine: An Approachable Open Tool to Clean Research Data

2022· article· en· W4220931242 on OpenAlexaffvenue
Meg Miller, Natalie Vielfaure

Bibliographic record

VenueBulletin - Association of Canadian Map Libraries and Archives (ACMLA) · 2022
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsContext (archaeology)Computer scienceWorld Wide WebData scienceOpen dataOpen sourceProgramming languageGeology

Abstract

fetched live from OpenAlex

This review provides an overview of data cleaning tools and discusses why and how OpenRefine has been an effective tool in the delivery of one-shot instructional sessions on data cleaning in an academic library context.

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)0.086
metaresearch head score (Gemma)0.207
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0860.207
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0210.015
Science and technology studies0.0040.003
Scholarly communication0.0120.017
Open science0.0060.020
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0240.019

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 head0.171
GPT teacher head0.345
Teacher spread0.174 · 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
Domainnot available
GenreSoftware

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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

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