Bibliographic record
Abstract
‘principle’, and old faithfuls like ‘God’, ‘fire’, and ‘war’, and a raft of terms like ‘statement’, ‘proposition’, ‘account’, ‘word’, ‘law’ (the preference of Marcovich), and the like. Then add to these ‘measure’ (Freeman), and ‘formula’ or ‘plan’ (Kirk), a formula or plan which he finishes up equating with ‘structure’, a structure he finds ‘corporeal’ in nature; 2 and no doubt many more that have escaped my attention. The technique I shall be adopting will be that of the ‘process of residues’ beloved of John Stuart Mill, in which I shall do all that I can to point out the impossibilities and high improbabilities running in the pack, in the hope that the residue which survives my strictures lies somewhere on a spectrum ranging from low improbability to low possibility to – dare we even mention it? – moderate to high possibility. Let me lay out my hermeneutical assumptions at once, so that you can start sharpening your weapons without further ado. – I shall be talking about the use of the word logos in DK fragments 1, 2, 31b, 39, 45, 50, 87, 108, and 115, but especially 1, 2 and 50.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".