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Record W1492651800 · doi:10.18533/journal.v3i3.388

Youth Stakeholders in Neighbourhood Revitalization: A Case Example

2014· article· en· W1492651800 on OpenAlexaffabout
Christine A. Walsh, Jennifer Hewson, Micheal L. Shier, Edwin Estuardo Morales

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNeighbourhood (mathematics)Participatory action researchYouth engagementYouth participationPositive Youth DevelopmentCommunity engagementCitizen journalismPublic relationsThe artsCivic engagementSociologyProcess (computing)Action researchCommunity participationPsychologyPolitical sciencePedagogyDevelopmental psychologySocioeconomics

Abstract

fetched live from OpenAlex

From publisher: Most studies describing youth engagement, focus on the positive aspects for youth development and the individual benefits associated with participation in youth engagement activities. Receiving less attention within the literature is research investigating the benefit of youth engagement for the wider community. This paper describes and analyses the process of developing and implementing a participatory action arts based research project in one community in Calgary, Canada with adolescent youth. Our findings suggest that utilizing a participatory research process for youth engagement can help support a more comprehensive understanding of the significance of youth participation beyond individual measures of youth development. Discussion of challenges and outcomes is provided for replicability of the study design and process in other settings.

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)0.003
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.004
Scholarly communication0.0040.002
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.530
GPT teacher head0.497
Teacher spread0.032 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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