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Users, Programmers, and Statistical Software

2000· article· en· W2090439194 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computational and Graphical Statistics · 2000
Typearticle
Languageen
FieldComputer Science
TopicData Stream Mining Techniques
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsComputer scienceSoftware analyticsSoftware engineeringSoftwareProgrammerSoftware constructionData scienceSoftware developmentHuman–computer interactionProgramming language

Abstract

fetched live from OpenAlex

Abstract Statistical software provides essential support for statisticians and others who are analyzing data or doing research on new statistical techniques. Those supported typically regard themselves as “users” of the software, but as soon as they need to express their own ideas computationally, they in fact become “programmers.” Nothing is more important for the success of statistical software than enabling this transition from user to programmer, and on to gradually more ambitious software design. What does the user need? How can the design of statistical software help? This article presents a number of suggestions based on past experience and current research. The evolution of the S system reflects some of these opinions. Work on the Omegahat software provides a promising direction for future systems that reflect similar motivations.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.699
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.257
Teacher spread0.247 · 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