Bibliographic record
Abstract
As more and more historical records are digitized, having a way to quickly analyze large volumes of tabular data makes research faster and more effective. R is a programming language with strengths in statistical analyses. As such, it can be used to complete quantitative analysis on historical sources, including but not limited to statistical tests. Because you can repeatedly re-run the same code on the same sources, R lets you analyze data quickly and produces repeatable results. Because you can save your code, R lets you re-purpose or revise functions for future projects, making it a flexible part of your toolkit. This tutorial presumes no prior knowledge of R. It will go through some of the basic functions of R and serves as an introduction to the language. It will take you through the installation process, explain some of the tools that you can use in R, as well as explain how to work with data sets while doing research. The tutorial will do so by going through a series of mini-lessons that will show the kinds of sources R works well with and examples of how to do calculations to find information that could be relevant to historical research. The lesson will also cover different input methods for R such as matrices and using CSV files.
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 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 | 0.018 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.280 | 0.255 |
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".