Bibliographic record
Abstract
Astronomical tablets have been a major focus of study by historians of science since the middle of the 20th century. Building upon the pioneering work of E. S. Kennedy, O. Neugebauer, and others, recent scholars have applied a range of techniques drawn from the exact sciences (e.g., computer-aided statistical analysis of tabular data to uncover the mathematical functions underlying a table’s construction), cognitive studies (e.g., examination of how practitioners use a table and what makes a table user-friendly or not), and manuscript studies (e.g., studies of tabular layout and visual clues to the use of tables, such as the presence of color as an indicator or specific final digits to numbers in the column of a table that indicate the nature of the table) to answer questions about the construction, use, and transmission of astronomical tables and the astronomical knowledge that they incorporate. For the past 20 years or so, José Chabás, often working in collaboration with B. R. Goldstein, has been at the forefront of work on early European astronomical tables. Reviewed by: John Steele, Published Online (2022-07-31)Copyright © 2022 by John SteeleArticle PDF Link: https://jps.library.utoronto.ca/index.php/aestimatio/article/view/39096/29783 Corresponding Author: John Steele, Brown UniversityE-Mail: john_steele@brown.edu
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".