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Record W3180967483

Designing urban parks: Theory and practice

2000· dissertation· en· W3180967483 on OpenAlexaboutno aff
Sassan Seyed-Kalal

Bibliographic record

VenueThe Atrium (University of Guelph) · 2000
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningArchitectural engineeringGeographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

This thesis was a step towards understanding the theory and practice of urban park design in the contemporary urban environment. The goal of this thesis was to investigate current ideas pertaining to urban parks, focusing on concept/philosophy, culture, ecology and recreation as the criteria of analysis, in order to provide a sound background for the design of Downsview Park, Toronto, Ontario. The thesis approach included explorations from research to design. The first part of the thesis consists of a literature review, and analysis and critique of the design of twelve selected urban parks as case studies. Patterns and traces of change in urban parks during the last century in Europe and North America were investigated. Goals drawn from the synthesis of these studies provided a background for a design for Downsview Park and in response to these goals a design was undertaken. From the Downsview Park design exploration, it was concluded that urban park design of today needs interactive, open-ended, flexible and inclusive approaches and techniques with the emphasis being on process. The final conclusion drawn front the thesis as a whole is that three issues, culture and nature, diversity, and space and time, emerge as central issues that present challenges in the design of urban parks in the contemporary and future urban environment.

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)0.012
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0050.024
Scholarly communication0.0130.010
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2000
Admission routes1
Has abstractyes

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