Reclaiming the Enlightenment: Toward a Politics of Radical Engagement
Bibliographic record
Abstract
Reclaiming the Enlightenment: Toward a Politics of Radical Engagement, Stephen Eric Bronner, New York: Columbia University Press, 2004, pp. xiii, 181. I am very sympathetic to the project that Stephen Eric Bronner undertakes in this book. As someone inspired by the progressive potential of the Enlightenment, I find myself constantly on the defensive in a world deeply suspicious of the dead white men of eighteenth-century Europe. So I welcome a vigorous and uncompromising defence of those men and the ideals and values they stood for. For those of us who already see ourselves as working to further that tradition, this book is an inspiring call to keep up the good work. But for those who have been hesitant in their enthusiasm for the Enlightenment, this book will be more like a red flag waved in their face, confirming many of their suspicions of Western rationalism. Theorists of recognition, identity, post-colonialism, post-modernism, republicanism, communitarianism, post-secularism and multiple-modernities, are just some of the people who will not find this book very congenial. Ultimately, Bronner's uncompromising defence of the Enlightenment is less successful than it might have been.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".