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Record W7073915983

Governing Greenbelts in Southern Ontario and the Frankfurt Rhine-Main Region: an Institutional Perspective

2020· dissertation· en· W7073915983 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2020
Typedissertation
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governancePerspective (graphical)Public policyRegional planningGovernment (linguistics)Regional policyPolicy analysisNational Policy
DOInot available

Abstract

fetched live from OpenAlex

In the last 30 years, a new generation of greenbelts has emerged in planning practice. These recent greenbelts have multi-functional policy goals and are often part of comprehensive regional land-use planning frameworks designed to manage regional growth more effectively. However, these regional greenbelts are increasingly under threat from suburban low-density development and the expansion of infrastructure networks, and their governance is embedded in complex institutional arrangements. These evolving circumstances create considerable challenges for policymakers seeking solutions to effectively govern these regional greenbelts. This study explores how institutional arrangements shape the governance of regional greenbelts in the Greater Golden Horseshoe region of Southern Ontario, Canada, and in the Frankfurt Rhine-Main region, Germany, as well as how these greenspaces could be more effectively managed in the future. The study shows that addressing the complex interactions between institutions and stakeholders involved in greenbelt management creates significant difficulties in coordinating policy implementation across different policy levels, policy fields and jurisdictions. Thus, this study reveals that the current institutional arrangements supporting new generation greenbelts cannot fully deliver on their ambitious policy objectives. To overcome these problems and to effectively manage these greenspaces, this study points to institutional design reforms needed for a new generation of greenbelts.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
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
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.177
Teacher spread0.166 · 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
Published2020
Admission routes1
Has abstractyes

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