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Record W2295368655 · doi:10.14288/1.0108858

How built environment affects wellbeing based on students’ preference for different cafes in the UBC Vancouver campus

2015· article· en· W2295368655 on OpenAlexaboutno aff
Lal Koyuncu, Caitlin Johnston, Astrid Arlove, Nathaly Uribe

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPreferencePsychologyMathematics

Abstract

fetched live from OpenAlex

The University of British Columbia in Vancouver has 29 projects under construction as of April 2015, ranging from academic lands to public spaces (UBC Campus and Community Planning, 2015). Research has shown that built environments have both direct and indirect effects on psychological well-being and mental health, one of which is how crowding and elevated noise levels hoist psychological distress (Ewans, 2003). This study examines how built environments (e.g. social spaces, access to nature) influence the well-being of students at UBC. This research focuses on students’ preferences of UBC Food Services Cafes and assumes that students prefer spaces in which they feel good/comfortable (Van Kamp et al., 2003). Using a correlational study, we first surveyed 82 UBC students and asked them to identify a favourite, mediocre and an avoided cafe. The results showed preference levels to Stir it Up, Neville’s, and Sauder Exchange Cafe, respectively. Then, we measured a pre-selected combination of eight factors of the built environments in these spaces by interviewing 20 participants per selected site. We finally analyzed data to find a correlation between specific factors of the built environment and students’ preferences. We did not obtain statistically significant results regarding any of the eight factors, however we found a trend of preference for lighting, location, less crowding and reduced noise levels. Thus we suggest that UBC needs to make sure, for future construction of UBC Food Services Cafes, to build them in highly populated class locations that have great lighting and are spacious. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.238
Teacher spread0.209 · 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 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
Published2015
Admission routes1
Has abstractyes

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