Bibliographic record
Abstract
This first chapter introduces readers to the basics of R. It provides the minimum of information that is needed for running the calculations that are described in later chapters. The first section may cover most of what is immediately necessary. The rest of the chapter may be used as a reference. Chapter 14 extends this material considerably. Most of the R commands will run without change in S-PLUS. An overview of R A short R session R must be installed! An up-to-date version of R may be downloaded from a Comprehensive R Archive Network (CRAN) mirror site. There are links at http://cran.r-project.org/. Installation instructions are provided at the web site for installing R in Windows, Unix, Linux, and version 10 of the Macintosh operating system. For most Windows users, R can be installed by clicking on the icon that appears on the desktop once the Windows setup program has been downloaded from CRAN. An installation program will then guide the user through the process. By default, an R icon will be placed on the user's desktop. The R system can be started by double-clicking on that icon. Various contributed packages extend the capabilities of R. A number of these are a part of the standard R distribution, but a number are not. Many data sets that are mentioned in this book have been collected into our DAAG package that is available from CRAN sites. This and other such packages can be readily installed, from an R session, via a live internet connection. Details are given below, immediately prior to Subsection 1.1.2.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.155 | 0.163 |
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".