Bibliographic record
Abstract
Professor of Philosophy In what follows, translations of primary texts for which no English translation are cited are my own. In general, I have provided translations of texts that appear in the body of my exposition and have opted to leave any material provided in the footnotes in its original language. The reader should also note that because the use of German in academic texts was still relatively novel in the eighteenth century, there are wide variations in spelling and grammatical conventions between authors, or even within the works of a single author. Since these do not often interfere with understanding, I have chosen not to standardize, modernize, or otherwise amend these texts. For ease of reference, I have made use of the following abbreviations and, when available, translations for frequently cited texts (for full bibliographical references, consult the Bibliography). I have made use of the following editions of, and abbreviations for, Wolff’s various publications. In cases where it is necessary to cite a specific edition of a text, I have introduced the convention of appending a subscript number to the abbreviation indicating the edition of the text concerned. Texts are cited by page or section number, as appropriate, unless indicated otherwise here.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.699 | 0.557 |
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".