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Youth and their Multiple Relationships with the City: Experiences of Exclusion and Belonging in Montréal

2020· book-chapter· en· W3028621162 on OpenAlexaboutno aff
Natasha Blanchet‐Cohen, Juan Torres, Geneviève Grégoire-Labrecque

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyGeographyPsychology

Abstract

fetched live from OpenAlex

Abstract This chapter examines how young people relate to and engage with their city. Framed by a sociological approach to childhood, we assert that young people are competent social actors, living a complex relationship with their urban environment, while facing paternalism. The study draws on participatory activities including focus group discussions, neighbourhood walks, city mapping and song and video creation with 54 youth aged 9–17 years from six areas of Montréal (Canada). Our findings point to young people’s mixed experiences and views of Montréal. On the one hand, the city is experienced as unwelcoming, excluding, homogenising and stressful. Among recreational facilities, mental health services and venues to hang out, there is little that meets youth’s specific needs and aspirations. They also pointed out the inequalities across neighbourhoods, pressures to fit into uniformising models, the limitations of gender roles and a lack of support from adults. On the other hand, youth are responding to and shaping their environment by seeking belonging in the city. They question the inequalities and homogenising forces, seek meaning in places and community and value relationships and diversity. We contend that moving towards child–youth friendly cities calls for better listening to youth to enhance the type of opportunities that reflect their needs and aspirations, while providing for inclusive cities that feature alternative forms of citizenship, accessibility to local places, diversity and community.

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 score0.172
Threshold uncertainty score0.961

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 communication0.0000.000
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 head0.057
GPT teacher head0.245
Teacher spread0.189 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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