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Record W2161324583 · doi:10.1080/14616696.2010.523476

SHIFTING INEQUALITIES

2010· article· en· W2161324583 on OpenAlexaff
Dietlind Stolle, Marc Hooghe

Bibliographic record

VenueEuropean Societies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsInequalitySociologyPolitical scienceMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

ABSTRACT Participation patterns in industrialized democracies have changed considerably in the last couple of decades. While institutionalized forms of participation (e.g., party membership) are declining, we can observe a rise in the occurrence of non-institutionalized forms of political participation. In this article we pose the question of what the effect of this trend has been for patterns of political stratification during the period 1974–2002 using the Political Action Survey as well as the European Social Survey. It can be observed that gender differences have been substantially reduced and in some cases even reversed for non-institutionalized participation and women tend to be more active in these forms than men. Younger age groups also clearly have a preference for non-institutionalized forms. Stratification based on education, however, remains the same compared to the 1970s. These findings are confirmed by a longitudinal analysis of Dutch Election Studies data for the period 1971–1998. We conclude that the emergence of new forms of political participation might have reduced age and gender based inequalities; however, it does not offer a solution for inequalities based on education.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.001

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 head0.038
GPT teacher head0.334
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations188
Published2010
Admission routes1
Has abstractyes

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