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Record W2735106831 · doi:10.1080/01596306.2017.1351920

The constraints of youth: young people, active citizenship and the experience of marginalisation

2017· article· en· W2735106831 on OpenAlexfundno aff
Andrew Hickey, Tanya Pauli-Myler

Bibliographic record

VenueDiscourse Studies in the Cultural Politics of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLocal governmentCitizenshipGovernment (linguistics)Constraint (computer-aided design)Public relationsFocus groupActive citizenshipPerceptionPolitical sciencePublic administrationSociologyPoliticsPsychologyLawEngineering

Abstract

fetched live from OpenAlex

This paper charts the experiences of a group of young people and their involvement in a local government initiative to engage young people in public decision-making. Activated through a youth leaders’ council that sought to influence and inform local government decision-making, the participating young people were given responsibility for enacting focus projects in collaboration with local government personnel. However, this method of simply bringing young people together with local government decision-makers did not automatically alter the way that decisions came to be made and ironically resulted in interactions that went some way to further reinforce existing perceptions of young people as incapable in situations of public administration. This paper reports on a case example detailing an interaction between the youth leaders and local government councillors, and will suggest that the experience of the young people involved in the youth leaders’ council can be understood against a dynamic of ‘constraint’.

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)0.007
metaresearch head score (Gemma)0.008
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0180.028
Scholarly communication0.0110.008
Open science0.0010.015
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.444
Teacher spread0.352 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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