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Record W4235115320 · doi:10.7202/1070560ar

Strong Democracy in Crisis: Promise or Peril? (Trevor Norris)

2020· article· en· W4235115320 on OpenAlexvenueno aff
Samantha Deane

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyPolitical sciencePolitical economyEpistemologyPhilosophyPositive economicsSociologyLawEconomicsPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.140
GPT teacher head0.402
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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