Review of Riccardo Saccenti’s Debating Medieval Natural Law: A Survey (Notre Dame, IN: University of Notre Dame Press, 2016)
Bibliographic record
Abstract
In this short monograph, Riccardo Saccenti surveys the various and competing interpretations of natural law and natural right from the late Middle Ages through the modern period. As “a survey,” the intention of this book is not so much to advance and defend a central thesis about natural law, but rather to paint a picture of how the various interpreters of natural law have responded to the most important primary texts (of Gratian, Thomas Aquinas, Duns Scotus, William of Ockham, and John Locke, for example) and to one another. One of the issues with which Saccenti is most concerned is the transition from the medieval understanding of natural law inspired by an Aristotelian concept of nature to a modern theory of natural individual rights. Where exactly does this transition begin and where is it more or less consummated? A reader of this book will be impressed by the complexity of this debate (among others) and the nuances of difference between its participants. Saccenti’s account of it succeeds in exposing the folly of overly simplistic readings of intellectual history that are ideologically motivated, and reminds us what a careful reading of primary and secondary sources can achieve.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".