Who Did What? The Roles of R Package Authors and How to Refer to Them
Bibliographic record
Abstract
Computational infrastructure for representing \npersons and citations has been available \nin R for several years, but has been restructured \nthrough enhanced classes "person" and \n"bibentry" in recent versions of R. The new \nfeatures include support for the specification of \nthe roles of package authors (e.g. maintainer, \nauthor, contributor, translator, etc.) and more \nflexible formatting/printing tools among various \nother improvements. Here, we introduce \nthe new classes and their methods and indicate \nhow this functionality is employed in the management \nof R packages. Specifically, we show \nhow the authors of R packages can be specified \nalong with their roles in package ´DESCRIPTION´ \nand/or ´CITATION´ files and the citations produced \nfrom it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.085 | 0.160 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".