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Record W7005489906

Reconstruction Ahead: School Streets and Street Reclamation in Ontario

2023· report· en· W7005489906 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2023
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsLand reclamationWork (physics)Front (military)Urban planning
DOInot available

Abstract

fetched live from OpenAlex

Streets are public spaces, yet they remain the domain of motorists. Contrary to this, people continue to challenge the narrative. One strategy has been through\nstreet reclamation efforts, referring to how streets can be reclaimed psychologically and physically from motorists to improve health, social connections, and community well-being. One type of street reclamation effort is called a School Street, a street experiment restricting vehicles on the street in front of a school at the start and end of the school day to create a car-free environment. School Streets have been organized globally in the past decade, with at least five implemented in Ontario since 2019. \n\nThe Reconstruction Ahead report investigates the implementation of Schools Streets in Ontario to reveal the implications for broader street reclamation efforts. To reach this goal, those who led every known School Street in Ontario and other relevant interest groups were interviewed to investigate the barriers faced and potential scaling solutions. The experience of the author of this report, as a School Street implementer in Kingston, Ontario, was also captured, ensuring the information was grounded by first-hand experience.\n\nBased on the findings, this report has documented barriers that hinder School Streets’ establishment, scale, and sustainability; proposed recommendations for\nmunicipalities to establish, scale, and sustain School Streets in Ontario; and names long-term implications for contemporary street reclamation efforts.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.562
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.015
GPT teacher head0.212
Teacher spread0.198 · 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.

Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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