Diderot’s Battle against Books : Books as Objects during the Enlightenment and Revolution
Bibliographic record
Abstract
Diderot against books ? The idea may seem surprising, considering the popular assumption that the Enlightenment, in which Diderot played a leading role, had always advocated education for all, and education necessarily required literacy and access to books. This optimistic vision reflects the desire of our contemporaries to see a linear progression from the eighteenth century to the Revolution and ultimately to the Republican values of present-day France. In fact, Diderot and his fellow philosophes did not set out to democratize knowledge. Instead, the rise of literacy during their lifetime alarmed them ; they denounced the quantity of books in circulation and the populace's avidity for them. It was only in the last years of the eighteenth century that reformers such as Benjamin Franklin and the marquis de Condorcet saw access to books as a desirable goal for a society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".