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Record W3107518029

Urbanizing the academy: The University of British Columbia's planning, development and sustainability story

2020· dissertation· en· W3107518029 on OpenAlexfundaboutno aff
Mike Wakely

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typedissertation
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
FundersUniversity of Manitoba
KeywordsSustainabilityUrban planningEnvironmental planningGeographyEngineeringPolitical scienceLibrary scienceRegional scienceCivil engineeringComputer scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Universities are traditionally focused on education and research, yet in a shift toward more entrepreneurial activities, some universities are using their large physical institutions and land base for land development projects and to demonstrate sustainability initiatives. While a move to market-based activities has been criticized for straying too far from the academic mission at the core of a university, supporters point to the new revenue stream from land development as a means to contribute to the University’s academic mission. As a politically autonomous institution from neighbouring municipalities and the regional government, and with its land use and permitting authority, UBC is ambitiously undertaking major urban development projects on its land. To understand how UBC arrived at its current context, this case study focuses on the key features, figures and processes of property development, land use planning and sustainable development undertaken at the University. Using document analysis and semi-structured interviews, this practicum includes a background study and provides insights specific to UBC’s experience as well as highlights relevant lessons for other universities seeking to engage in property development and integrate sustainable development initiatives into their design and operations.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.052
GPT teacher head0.291
Teacher spread0.240 · 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.

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
Published2020
Admission routes2
Has abstractyes

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