Bibliographic record
Abstract
Promise or Peril? is a collection of critical essays that extends Benjamin Barber's work on strong democracy into the complex terrain of contemporary democratic theory.Barber is best known for his 1984 book Strong Democracy: Participatory Politics for a New Age, but this is not to say his scholarship ends here; Barber has devoted his life to prefacing, re-framing, and re-imagining what it might mean to generate democratic communities.Serving a dual purpose, this text is also a Festschrift of sorts: each essay honors Barber's friendship by taking up his characteristic hope that the future will be paved with more decency and democracy.Collectively, the twelve essays in Strong Democracy in Crisis marshal three sets of concerns regarding the realization of strong democracy: 1) the very possibility of practicing strong democracy; 2) the influence of Barber's Rousseauian outlook on the desirability of Barber's political vision; and 3) the plausibility of enacting strong democracy in the increasingly commercial and globalized world.The text also features three disciplinary interludes that collectively problematize the practice of making political theory.After summarizing the key points of each chapter, I will highlight a few gaps in the volume's presentation of democratic crises.In calling attention to these gaps, my intent is to point toward openings for continued scholarship on the possibility and plausibility of strong democracy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".