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Record W1555038297 · doi:10.18357/ijcyfs43.1201312622

DELIBERATION MODELS FEATURING YOUTH PARTICIPATION

2013· article· en· W1555038297 on OpenAlexvenueaboutno aff
Denise Bulling, Lyn Carson, Mark DeKraai, Alexis Garcia, Harri Raisio

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsDeliberationDeliberative democracyLegitimacyPolitical scienceStyle (visual arts)DemocracyTokenismCitizen journalismPsychologySociologyPublic relationsSocial psychologyPoliticsGeographyLaw

Abstract

fetched live from OpenAlex

<p><span style="font-family: Times New Roman; font-size: small;">There is a growing trend among developed countries to increase the participation of youth in societal and institutional decision-making. The challenge is to move away from an illusion of participation (tokenism) to genuine youth influence. This article transfers knowledge of a relatively new theory to the fields of youth engagement and community development.<span style="color: #ff0000;"> </span>We pose deliberative democracy as a model to build bridges between youth and decision-makers. This concrete approach offers a platform for youth and adults to engage in a learning process as equal citizens and proactive leaders. <span style="color: #000000;">Deliberative democracy can be understood as an umbrella term for different models of public deliberation. These models attempt to create carefully detailed conditions for increasing the legitimacy of decisions made through deliberation. Deliberative models that feature youth participation include youth juries, dialogue days between young people and decision-makers, and adult-youth participatory forums where the youth voice is usually a minority. </span>We explore the role of relationships, collaboration, and leadership in generating democratic spaces for the inclusion of youth in policy formation and reform.<span style="color: #ff0000;"> </span><span style="color: #000000;">The challenges associated with engaging youth are discussed along with examples of models from Australia, Finland, Canada, and the United States that promote effective youth engagement.</span></span></p>

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.237

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 communication0.0000.001
Open science0.0000.000
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 head0.083
GPT teacher head0.349
Teacher spread0.266 · 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 designQualitative
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

Citations11
Published2013
Admission routes2
Has abstractyes

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