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Record W7036519852

Beyond Bricks and Books: Exploring the Quality of Campus Open Spaces

2024· other· en· W7036519852 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleVariety (cybernetics)University campusSpace (punctuation)Public open spaceBuilt environmentScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

The impact of physical landscapes on people's health is widely acknowledged, yet research on open spaces within institutional campuses is limited. This Master’s report delves into this gap by evaluating four campus open spaces at the University of Toronto – St. George and Queen’s University. The study aims to address the following objectives: 1) Determine policy directions within the campus masterplans and applicable municipal policies with respect to open space, 2) Utilize an evaluation framework to assess the quality of existing open space within the two higher education institutions through post-occupancy evaluation methods, and 3) Provide recommendations to improve and better incorporate open spaces within higher education institutions. Utilizing a mixed-methods, collective case study design, the research utilizes an evaluation framework adapted from Damone (2019) to assess two park sites and two street environments across both universities. At the University of Toronto – St. George, St. George Street as well as Back Campus and Hart House Circle were selected, while at Queen’s University, University Avenue as well as Nixon and Benedickson Fields were selected. Each of the 21 criteria was assessed with a Likert scale and accompanying notes provided by a variety of observers. Results indicate that the park sites generally outscored street environments across most criteria, with the University of Toronto performing better overall than Queen’s University. Notably, Accessibility of Environment and Safety sections receive high scores due to their central campus location and commitment from both institutions to such in their CMPs. Engagement with the Environment and Access to Nature criteria vary, with park sites generally performing better. With the Community Engagement and Amenities sections also demonstrating lower scores for street environments compared to park sites. Both universities were recommended to further collaboration with their respective municipalities and develop urban forest management plans and consolidate land resources as well as establish living labs to catalyze innovative open space development. For Queen's University in particular, it is recommended that along University Avenue, planters and trees be placed adjacent to the edge of the road, acting as both a visual and physical buffer. As well as throughout Nixon and Benedickson fields, a variety of adaptable seating options should be provided to accommodate programmable as well as passive interaction spaces. While at the University of Toronto - St. George it is recommended that along St. George Street, a variety of seating options be provided and that bulletin or notice boards be placed throughout Back Campus Fields along with low impact outdoor fitness equipment. Overall, this study underscores the significance of open spaces in campus design and provides valuable insights into their assessment and improvement. Implementing recommendations derived from this research could enhance campus environments, fostering cultural integration and improving the mental well-being of students, faculty, and staff.

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)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.006
Scholarly communication0.0100.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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 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
Published2024
Admission routes1
Has abstractyes

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