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Record W6981784402

An exploratory study of popular interpretations of sustainable urban green space design

2006· dissertation· en· W6981784402 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2006
Typedissertation
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsInterpretation (philosophy)Exploratory researchSpace (punctuation)SituatedSustainabilityUrban green spaceSet (abstract data type)Urban designUrban sustainability
DOInot available

Abstract

fetched live from OpenAlex

This study evaluates the perceptions and opinions of site users for sustainable, urban green space design priorities, as defined in recent academic publications. For investigating, analyzing and prioritizing the lay interpretation of sustainable, urban green space, a tool was developed and applied. Local knowledge was gathered via on-site survey and incorporated by means of a mixed analysis. Analysis of results generated a set of sustainable, urban green space design priorities. Findings indicate that a consistent interpretation of sustainability that is primarily socio-ecologic and integrative in nature prevails among the user group of the Alf Hales Memorial Trail and John Galt Park Guelph, Ontario study site. Survey respondents prioritized 'Ecologic' and 'Participative' themes highly; 'Cognitive' and 'Equitable' themes moderately; and 'Economic' and 'Sensorial' as the lowest priority. Design strategies that are relevant and meaningful to users, who interact with the landscape, will enhance urban green space as valuable community resources now and for the future, and in this way, may be considered sustainable. The process of investigation may be applied to other similarly situated sites in southwestern Ontario.

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)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0090.009
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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