Argumentum ex Cartesio: A Study of Descartes’ Employment of Arguments in His Meditations on First Philosophy
Bibliographic record
Abstract
In his Regulae (Rules for the Direction of the Understanding), Descartes focuses on Arithmetic and Geometry as the methodological model for argumentation and learning generally. As a result, it is generally assumed that his Meditations on First Philosophy employs the deductive method utilized in Mathematics. Part of the difficulty in understanding the method of the Meditations stems from the fact the nowhere in the Meditations does Descartes explain the method he employs in this work. In fact, Descartes addresses the method of the Meditations in only one place, namely, in the Replies to the Second Set of Objections, where he contrasts the method of Geometry (which he refers to as ‘synthesis’) with the method of the Meditations (which he refers to as ‘analysis’). In my paper, I turn to the Descartes’ Dreaming/ Waking argument in the fourth and fifth paragraphs of the first meditation to illustrate how scholars have erred in their critical exegetical efforts, when they regard the Meditations as utilizing a logical mathematical-type approach in arguments in the search after truth. In the second half of my paper, I focus on Descartes’ method of ‘analysis’, the only method that he insists he employs in his Meditations.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".