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Record W7043028725

Rethinking Transportation Planning - Citizen Participation and Inner Suburban Social Justice in Toronto

2015· other· en· W7043028725 on OpenAlexaffabout

Bibliographic record

VenueYork University Digital Library (York University) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsTransportation planningPublic transportUrban planningAgency (philosophy)Context (archaeology)PoliticsRegional planningStrategic planning
DOInot available

Abstract

fetched live from OpenAlex

This paper examines how to move towards a more socially just transit system in Toronto. Much of the conversation regarding transportation focuses on the needs of the downtown core. The needs of inner suburban residents, despite dependency on public transportation, are not fully taken into consideration in efforts to improve transit. Employing critical planning theory as a theoretical lens, I examine the transportation planning process with a focus on high-rise neighbourhoods in inner suburban districts by analyzing the transportation planning process of the Finch West Light Rail Transit (LRT) project. Examining mobility as a basic social justice issue reveals that the unequal distribution of transit services is connected to social and political processes that lead to uneven development and socio-spatial polarization in Toronto. \n \nThe main objective of the paper is to identify the actors involved in the Finch West LRT planning process and the extent to which citizens have agency in transit planning in the context of the Finch West LRT planning process. I also seek to determine the role of politics in the decision-making process in urban planning, and finally, identify some strategies for the building of a socially just transit system. \n \nFor my research, I undertook a review of urban planning literature with the goal of understanding the complexity of civic engagement, social justice and politics in relation to transportation planning. In addition, I conducted semi-structured interviews with politicians, planners and residents, analyses of key planning and transportation documents, observation of community meetings, and direct site observation of Finch West. \n \nMy research uncovered key characteristics of inner suburbs that were present in Finch West, such as food deserts, physical decay, increased poverty, inadequate services, lack of employment, lack of a sense of safety, and high crime rates. My findings regarding transportation along Finch West reveal enormous congestion, large parking lots that discourage walkability, and a high dependency on public transport. The bus routes suffer from overcrowding with ridership over capacity, and reduced services on weekends. I conclude that the political instability at the municipal and provincial levels is an obstacle in creating a unified vision and subsequent plan for action. Furthermore, although citizens were informed of the Finch LRT plan, there were few opportunities to contribute to the consultation process at the beginning stages. Lastly, I suggest an approach to civic participation where planners and residents see themselves as both active educators and active learners. \n \nI suggest that transit does require a more equal distribution of goods across the city, and movement towards a socially just transit system would enable citizens to contribute to the decision making process to a greater degree than is currently conceived. I conclude that planning processes, when carried out in a critical manner, can address social disparity, and to do so, greater citizen participation is required.

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)0.004
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 score0.088
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0190.019
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0010.002
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 head0.024
GPT teacher head0.214
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 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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