Kingston Student Housing Co‐operative: Budgeting and Governance in a Global Crisis<sup>*</sup>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".