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Record W2196546277

The Challenge of Youth Engagement in Local Government: Exploring the Use of Youth Councils in Amherst and Halifax Regional Municipality, Nova Scotia

2014· article· en· W2196546277 on OpenAlexaboutno aff
Katelynn Northam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Education Environments
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)Youth engagementLocal governmentPolitical scienceGovernment (linguistics)Public administrationSociologyPublic relationsEthnologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Youth councils are an increasingly popular tool that both government and non-governmental organizations use to inform policy and program development, to increase the participation of young people, and to improve the connection of youth to their communities and to civic life more broadly. In this thesis, the youth engagement experiences of local governments in Amherst, Nova Scotia, and Halifax Regional Municipality, Nova Scotia are examined. Both municipalities initiated youth councils in recent years in an attempt to consult on issues affecting youth in their communities. The two communities have experienced varied degrees of success in implementing these strategies. In broad terms, youth councils operated more successfully in the smaller, more self-contained community of Amherst than in the more sprawling urban municipality of the Halifax area. The divergent experiences of these two municipalities inform a discussion about the merits of youth councils as a tool for engagement for local governments. It is concluded that while youth councils can be both effective in terms of achieving immediate objectives, success is not easily reproduced in all scenarios and depends to a large extent on the characteristics of the community itself, the level of support from adults and facilitators, and the ability of the councils to meet their objectives and thus achieve legitimacy among stakeholders, creating a positive feedback look which engenders further effectiveness.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.829

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.008
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.311
GPT teacher head0.324
Teacher spread0.013 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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