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.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.570 | 0.392 |
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".