Bibliographic record
Abstract
Breaking changes apply_table_theme() was removed, since it was an experimental feature that is no longer used in any package. Changes display(), print_md() and print_html() get a .table method. get_predicted() now supports chisq.test(), and returns the expected frequencies. export_table() gains better support for the tinytable package. Use format = "tt" to export tables into the tinytable-format. This can also be used with grouped tables, i.e. by = "group". export_table() gains arguments row_groups and column_groups, to group rows and columns in the exported table. Column groups currently only work for format = "tt". If arguments title, subtitle and footer in export_table() are set to an empty string "", no titles/subtitles/footers are printed, even if present as attributes. Added a .lavaan method for is_converged(). The formerly internal function to extract various information about mixed models is now exported as get_mixed_info(). Bug fixes Fixed issue with models of class selection with multiple response variables. Fixed issue in get_datagrid() for factors with = in their levels. Fixed issue in find_random() for multivariate response models of class brms with special response options. Fixed issue in several functions for certain betareg-models that contained a "mu" component instead of "mean". Fixed CRAN check issues on M1 Macs.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.084 | 0.016 |
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; both teacher heads agree on what is shown here.
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".