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Reproducible Econometrics Using R

2019· book· en· W2912947211 on OpenAlexaff
Jeffrey S. Racine

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRange (aeronautics)Computer scienceSoftwareEconometricsOpen sourceOpen source softwareData scienceMathematicsEngineeringProgramming language

Abstract

fetched live from OpenAlex

Abstract This book is designed to facilitate reproducibility in Econometrics. It does so by using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible research. Illustrative examples are provided throughout, and a range of topics are covered. Assignments, exams, slides, and a solution manual are available for instructors.

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)0.020
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.980
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.134
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0010.003
Scholarly communication0.0070.004
Open science0.0030.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.1070.107

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 head0.492
GPT teacher head0.476
Teacher spread0.016 · 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
GenreMethods

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

Citations9
Published2019
Admission routes1
Has abstractyes

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