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Record W2805506413 · doi:10.1080/14733285.2018.1482409

Eyes on the alley: children’s appropriation of alley space in Riverdale, Toronto

2018· article· en· W2805506413 on OpenAlexaffabout
Alexander Furneaux, Kevin Manaugh

Bibliographic record

VenueChildren s Geographies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
Fundersnot available
KeywordsAppropriationAlleyNeighbourhood (mathematics)SituatedSpace (punctuation)PsychologyContext (archaeology)Social psychologySociologyDevelopmental psychologyGeography

Abstract

fetched live from OpenAlex

Recent research suggests that numerous positive physical, cognitive, and social benefits can be derived from independent mobility and play agency amongst children, necessitating an understanding of how physical and social environments facilitate this development. This study involved parents, and children aged 9–13 from twelve households in the neighbourhood of Riverdale in Toronto. Using a mapping exercise to instigate discussion, participants were asked to describe where, how, and with whom play occurs in their neighbourhood. A reoccurring theme emerged amongst households that border a back alley where parents perceived this space as safer allowing them to grant greater independent mobility to their children and use this space as an intermediary tool to prepare their children for greater independence. For children, this space serves as one of creative appropriation, granting them access to more space and friends to play with. Situated within the context of age-friendly cities, this research identifies several socio-spatial qualities found in alleys that have the potential to contribute to the discussion of more inclusive city-building practice.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.406
Threshold uncertainty score0.818

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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