What's gained, what's lost? The paradox of using land value capture to fund arts spaces in Vancouver
Bibliographic record
Abstract
In recent years local governments in expensive cities have attempted to offset the displacement of arts spaces by funding new cultural infrastructure. One of the key policy tools they have turned to is land value capture, which allows greater height and density on new high-rise developments in exchange for social benefits. This study takes up the case of Vancouver, Canada where land value capture has been used to fund the arts through the city's Community Amenity Contributions (CAC) program. To examine the impact and evolution of Vancouver's CAC program, I use qualitative case studies of three major redevelopments in the 2010 decade that leveraged CACs to fund the arts, alongside planning documents and community reports. My findings show that the city helped arts organizations secure increasingly stable infrastructure, approving funding to purchase their buildings, cover long-term operating costs, and to build artist housing through a community land trust. At the same time, the arts community reported a significant loss of space in the private market, often in areas experiencing intense development pressure. Through the case of Vancouver, I show that land value capture leads to a paradoxical outcome—the same policy mechanism that protects artists from the market, relies on increasing property values that contribute to arts displacement. I conclude by suggesting that land value capture should be paired with protective policies for arts spaces that intervene on real estate speculation to avoid a net loss.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.022 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".