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Record W3173832994

Social Interaction and the Built Environment: A case study of university students in Waterloo, Ontario

2021· dissertation· en· W3173832994 on OpenAlexaboutno aff
Tharushe Jayaveer

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsBuilt environmentMathematics educationSociologyPsychologyEngineeringCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

In recent years, there have been rising calls for universities to develop policies that support student well-being due to the growing concern for mental health on campuses. One area of concern is the influence of the built environment on students’ mental health. A built environment that fosters social interaction is often recognized as a vital component in supporting well-being, friendship formation, academic achievement, self-identity and even knowledge creation. The literature has identified housing type, location, and quality as substantial determinants of students’ social lives and well-being. However, research has not yet studied the importance of housing and the built environment in shaping social interactions among university students in detail. 
\nIn this study, we examine the relationship between the role of the built environment, such as proximity to third places, on social interaction among students at the University of Waterloo. We particularly compare the degree of social interaction and connectedness and studying at third places like university libraries and coffeeshops and compare degree of social interaction and connectedness with students who study at home. We draw on unique time-series survey data that includes information from the same group of students collected over the course of the academic year (Fall 2018 to Summer 2019). The survey design allows us to draw potential conclusions about causal links between built form and indicators commonly associated with mental health, such as degree of social interaction and feelings of connectedness. 
\nThe survey includes information on students’ residential environments, built form, demography, and various indicators of social interactions and chance encounters. Through ordered logistic regression analysis, we found that students who study at coffeeshops and university libraries felt a higher degree of social connectedness, had more positive attitudes toward planned gatherings, and preferred living close to amenities compared to students who study at home. However, it is important to note that there were differences in these findings over the course of the academic year, and that programming, such as social events, were as important as built form in shaping indicators of well-being. 
\nThe empirical evidence from this research supports the notion that the use of third places heightens feelings toward social connectedness. The knowledge gained from investigating the relationship between the role of the built environment in influencing social interactions among the student population will be valuable to universities and planners to develop policies, programs, and initiatives to provide opportunities and create environments that support social connectedness. 
\nA crucial element of this research was to acknowledge the differences among students who study at third places and those who study at home. Though some students use third places to socialize, and feel connected, others may not. This research raises some questions – are there other alternative initiatives that can be taken beyond creating social built environments that could encourage students to engage in social interaction? This research emphasizes the important role of the built environment and programming in shaping students’ social interaction and well-being.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.597

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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
Published2021
Admission routes1
Has abstractyes

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