Transferable development rights : a policy analysis of a planning instrument and its application in Vancouver
Bibliographic record
Abstract
In this thesis I examine the planning tool most commonly known as the transfer of development rights (TDR) and discuss its application in Vancouver. Before addressing Vancouver's use of TDR, I establish the context of TDR use in North America, suggest appropriate policy objectives and constraints for TDR programs, and outline a series of operational decisions made in designing any TDR program. I proceed to evaluate Vancouver's TDR program in light of these discussions. I found that TDR programs can be effective tools for redistributing the costs and benefits of certain types of land use restrictions. However, TDR programs vary widely in their effects. Depending on the specific design of a given program, it can have very different implications. In Vancouver, the TDR program is a relatively minor adjunct to the process of heritage preservation. Like any planning tool, Vancouver's TDR program strikes a balance between various objectives. However, it can be generally stated that fairness or distributional concerns are prevalent in Vancouver's program. Specifically, the protection of property rights is one of the defining elements of the program. Vancouver's program has been marked by a strong discretionary component, which has tended to create high transactions costs. In recent years, though, transaction costs in Vancouver have gone down significantly. As transaction costs have decreased and the program has grown more fluid, the take-up rate of transferable density in Vancouver has increased. These trends are widely expected to continue, as Vancouver's transfer of density program further matures.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".