Three Dimensions of Capital Switching within the Real Estate Sector: A Canadian Case Study
Bibliographic record
Abstract
This article analyzes capital switching practices in the real estate development sector. The leading practitioners in Canada, the Canadian‐based real estate companies, are the subject of this inquiry. The tradability and divisibility of real estate properties enhance the growing similarity between real estate properties and other financial assets. It is appropriate to talk about portable real estate portfolios because companies constantly rearrange their property composition. This article emphasizes the ‘three dimensions of capital switching’. These dimensions unpack significant corporate considerations in the deployment and redeployment of capital, namely, mode of operation, property type and location. With respect to office development, the trajectory of the major real estate companies over the last 20 years has accommodated two notable shifts. First, these companies have focused on larger and newer properties, disposing of their smaller and older assets. Second, they have funneled a growing share of their capital assets into the top‐tier cities of the Canadian urban system, reducing investment in other metropolitan areas and disinvesting in the small‐to‐medium sized cities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".