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Record W4404292093 · doi:10.1111/1911-3838.12382

Kingston Student Housing Co‐operative: Budgeting and Governance in a Global Crisis<sup>*</sup>

2024· article· en· W4404292093 on OpenAlexaffvenueabout
Ryan Stack, Bertrand Malsch

Bibliographic record

VenueAccounting Perspectives · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsQueen's UniversityAcadia University
Fundersnot available
KeywordsCorporate governanceBusinessFinancial crisisPolitical scienceFinanceEconomicsMacroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT This field research case focuses on a housing cooperative based in Kingston, Ontario. The key issue is the need to craft a budget to support the general manager, Brent Bellamy, as he plans for the coming academic year. Because of the COVID‐19 pandemic, Queen's University suspended in‐person classes for the 2020–2021 school year. The Kingston Student Housing Co‐operative (KSHC) depends on student members who attend Queen's to stay in its rooms and participate in its operations and governance structure. With Queen's effectively shutting down, KSHC's key source of funds will be absent for the coming year. Brent needs help recalculating the budget for the coming year, leading to advice on some of his strategic options. He is also worried about other issues at KSHC: (1) the organization's long‐time bookkeeper has just given notice of her intent to retire, and he must evaluate the bookkeeping tasks that were her responsibility; (2) he has concerns about the governance structure, which sees a lack of continuity in the board of directors between years; and (3) KSHC was considering a significant build project prior to the COVID‐19 pandemic that Brent would like to advance. In considering these issues, Brent must acknowledge and respect the unique nature of a cooperative as a member‐owned nonprofit organization based on certain cooperative principles.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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
Published2024
Admission routes3
Has abstractyes

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