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Record W2889920831

Barriers to Growth Management: Local Challenges Implementing the Growth Plan for the Greater Golden Horseshoe

2018· dissertation· en· W2889920831 on OpenAlexaboutno aff
L. Michelle Lee

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsHorseshoe (symbol)Horseshoe crabGrowth managementPlan (archaeology)Environmental planningGeographyEnvironmental resource managementOperations managementEngineeringComputer scienceBiologyEcologyArchaeologyEnvironmental scienceCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

To combat urban sprawl and its negative effects on ecosystem services and human health, regional growth management and containment policies have been used with increased frequency to manage urban growth. Yet, local implementation of regional growth management planning policies across North America has had mixed success, often resulting in a mismatch between growth management planning objectives and the urban development reality. This research explores the reasons for the apparent mismatch by examining how barriers to local implementation are expressed, reinforced and perpetuated to prevent transformative change.
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\nUsing Ontario’s Growth Plan for the Greater Golden Horseshoe as a case study, the dissertation examines the barriers to implementation through a review of local contextual information and the perspectives of those tasked with implementing the Plan within three case study regions of the Greater Golden Horseshoe: Waterloo, Simcoe and Peterborough. A relational model of barriers reported in the literature is developed and tested against the barriers described by local planners, developers, the media, planning documents, and locally relevant academic literature and used to frame comparisons across case studies. Variations among the case studies are interpreted in light of the model using a conceptual framework that conceives barriers as institutions embedded within a hierarchical culture of planning. 
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\nCase study results reveal that barriers to local implementation vary across regions. This variation can be attributed to particular local contextual pressures and differences in local planning environments that influence how broader, societal barriers are understood, justified, managed and reinforced. Planning environments in the more rural and exurban case studies regions of Simcoe and Peterborough demonstrated similar belief systems, values and planning goals that obstructed local efforts to manage growth. These same regions faced particular growth and economic pressures that reinforced existing value systems and reduced the range of perceived planning solutions and approaches to growth management. In contrast, planning environments in the more urban Waterloo case study region, as well as urban single tier municipalities within the rural case study regions, demonstrated planning environments that were more open to innovative and assertive planning approaches to manage growth.
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\nThis research demonstrates how the interactions between local context and planning environments shape the interpretation and implementation of regional growth management plans. The research findings provide focal points for further research on growth management implementation by highlighting barriers and patterns of reinforcement that are less visible and rarely acknowledged in planning practice. As well, this research highlights the need for planning approaches that recognize the important role of the local planning environments in advancing growth management objectives. Failure to recognize and address the underlying barriers and their interdependencies may result in the development of regional growth management plans that fail to achieve their objectives.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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 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
Published2018
Admission routes1
Has abstractyes

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