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Record W4392902214 · doi:10.32920/25417438.v1

Cities for Youth: A Pragmatic Approach to Engaging Youth Throughout the Urban Planning Process

2024· preprint· en· W4392902214 on OpenAlexafffund

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsYouth engagementFutures contractLeverage (statistics)DeliverablePublic relationsCommunity engagementWork (physics)Urban planningProcess (computing)SociologyPublic engagementPolitical scienceBusinessEngineeringComputer science

Abstract

fetched live from OpenAlex

A wealth of perspectives exists in urban spaces; engagement opportunities in planning have the potential to collect theses perspectives and leverage them to shape urban futures. Recognizing the need for diverse and comprehensive community engagement opportunities in planning, this work looks towards how youth voices can be further integrated into the planning process. Youth represent active stakeholders in their communities, with a keen capacity to observe and recount their lived urban experienced. Existing literature details the challenges and opportunities that youth engagement represents. This research explores specific tools that urban planners and other allied professionals make use of when engaging youth-identifying communities. Through a series of semi structured interviews, this project led to the creation of an actionable youth engagement toolkit: Cities For Youth – Toolkit for Youth Engagement in Planning, This professional facing deliverable was supported by the production of a complementary podcast episode as part of the Spacing Radio series. Jointly, these two pieces look to expand on professional perspectives on youth engagement while bringing this issue to the forefront of urban journalism.

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)0.001
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 score0.072
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.059
GPT teacher head0.292
Teacher spread0.233 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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