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Party Reform

2016· book· en· W4248944426 on OpenAlexaboutno aff
Anika Gauja

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical sciencePoliticsScholarshipPublic administrationDemocratizationModernization theoryPublic relationsPower (physics)DemocracyLaw

Abstract

fetched live from OpenAlex

Abstract Party Reform is a new comparative study of the politics of party organization. The book provides a novel perspective in party scholarship and develops the concept of ‘reform’ as distinct from evolutionary and incremental processes of party change. As an outcome, reform is captured in deliberate and often very public changes to parties’ organizational rules and processes. As a process, it offers a party the opportunity to ‘rebrand’ and publicly alter its image, to emphasize certain strategic priorities over others, and to alter relationships of power within the party. Analysing the last ten years of party reform across a handful of established democracies including Australia, the United Kingdom, Canada, and Germany, the book examines what motivates political parties to undertake organizational reforms and how they go about this process. The book demonstrates that declining party memberships have had a fundamental effect on the way in which political parties ‘sell’ organizational reform: as part of a broader rhetoric of democratization, of re-engagement, and of modernization delivered to diverse audiences—both internal and external to the party. The chapters focus particularly on four key reform initiatives that begin to blur the traditional boundaries of party: the introduction of primaries, the changing meaning of party membership, issues-based online policy development, and community organizing campaigns. Using these cutting-edge developments as primary examples, this book provides a framework for understanding why, and how, reforms occur, and what the consequences might be in terms of participation and representation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.948
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.238
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations58
Published2016
Admission routes1
Has abstractyes

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