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Record W7015826964

Vandalism as a design consideration in urban parks: A case study of University Village Park

2003· dissertation· en· W7015826964 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2003
Typedissertation
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Urban parkPublic parkUrban designTerritorialityPedestrian
DOInot available

Abstract

fetched live from OpenAlex

Vandalism is reported to be a major problem for many urban parks. This thesis explores the relationship between vandalism and design through examination of a neighbourhood park in Guelph, Ontario. The purpose of the thesis is to examine the environmental and social variables that might predict neighbourhood park vandalism. Various concepts that relate to understanding vandalism and reducing it through physical design were explored in the literature, including vandalism, social psychological origins of vandalism, neighbourhood design, territoriality and, defensible space-crime prevention through environmental design (CPTED) concepts. From this literature review, three streams of data were collected: information about use of the park, obtained through a mail-out resident survey; information about vandalism in the park, collected from police reports, maintenance crews and observation; and information about physical factors of the park and neighbourhood that influence vandalism and its prevention, obtained by examination and assessment of the physical features of the neighbourhood. From these data sets, recommendations were prepared that suggest ways to reduce and prevent further vandalism in the park. These recommendations were reviewed with members of the community, with city staff responsible for park design and maintenance, and with the local police. Based on this review, recommendations for the park were modified. The study concludes with guidelines for the reduction of park vandalism through design, both in the rehabilitation of existing urban parks and the design of new ones.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.126
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.275
Teacher spread0.247 · 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.

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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