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Record W4393482895 · doi:10.5281/zenodo.831804

Replication Package For How The R Community Creates And Curates Knowledge: An Extended Study Of Stack Overflow And Mailing Lists

2017· dataset· en· W4393482895 on OpenAlexaff
Alexey Zagalsky, Daniel M. Germán, Margaret‐Anne Storey, Carlos Gomez-Teshima, Germán Poo-Caamaño

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typedataset
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsReplication (statistics)Stack (abstract data type)Computer scienceWorld Wide WebData scienceBiologyOperating systemVirology

Abstract

fetched live from OpenAlex

This dataset was used in the paper: "How the R Community Creates and Curates Knowledge: An Extended Study of Stack Overflow and Mailing Lists", Journal of Empirical Software Engineering, to appear.

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)0.017
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.995
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.091
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.006
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0050.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0430.039

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 head0.281
GPT teacher head0.431
Teacher spread0.150 · 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
DomainReproducibility
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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