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Record W4362668861 · doi:10.1017/s1049096523000239

Software Citations in Political Science

2023· article· en· W4362668861 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

VenuePS Political Science & Politics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicComputational and Text Analysis Methods
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsScripting languageSoftwareComputer sciencePoliticsSoftware engineeringData sciencePublic goodSoftware walkthroughPackage development processSocial software engineeringSoftware analyticsSoftware constructionSoftware peer reviewSoftware developmentWorld Wide WebPolitical scienceProgramming languageLaw

Abstract

fetched live from OpenAlex

ABSTRACT Political scientists rely on complex software to conduct research, and much of the software they use is written and distributed for free by other researchers. This article contends that creating and maintaining these public goods is costly for individual software developers but that it is not adequately incentivized by the academic community. We demonstrate that statistical software is used widely but rarely cited in political science, and we highlight a partial solution to this problem: software bibliographies. To facilitate their creation, we introduce softbib , an R package that scans analysis scripts, detects the software used in those scripts, and automatically creates bibliographies. We hope that recognizing the contribution of software developers to science will encourage more scholars to create public goods, which could yield important downstream benefits.

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)0.007
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.009
Science and technology studies0.0020.012
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.080
GPT teacher head0.461
Teacher spread0.381 · 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