Valuing Land: Welfare State Disinvestment, Inclusionary Zoning, and Justice
Bibliographic record
Abstract
This research asks what happens when the measures that will provide needed housing to the poor are also those that will displace other precariously housed people and disrupt valued social relations in neighbourhoods. To answer this question, it examines political and economic shifts in the welfare state, urban scale, and urban land use, through the lens of an inclusionary zoning land use policy in Vancouver, Canada. This policy trades additional land development rights to real estate developers in exchange for funds for public amenities, increasingly social or low-income housing. Based on sixteen months of ethnographic research in three neighbourhoods in Vancouver, this project finds that the land use policy in question has contradictory results. The policy produces small amounts of new low-income housing units in a time of diminished public funding. Yet, it also produces large condominium developments that contribute to speculation in the land and housing market, rising land values and rent rates, displacement of homes and communities, urban inequality, and urban conflict. \n\nThis project argues that disinvestment measures have resulted in a rescaling of the city as an arena of welfare state funding enacted by assembling land for investment and development. Further, it argues that while urban residents articulate alternative, often non-market, understandings of the value of land and place, the effect of these articulations is impeded by new state forms that rely on private capital. Throughout I examine the work of land use planning in assembling urban land into an investable commodity despite its existing uses. This work leads to conflicts between residents, real estate developers, non-profit housing agencies, and city government over competing conceptions of the value of land and place. As neighbourhoods change through new land investments, residents’ place making activities are overwhelmed, particularly those of residents who experience race and class inequality. In addition, since this policy implicates social housing providers in the gentrification of neighbourhoods, it also contributes to resident distrust of the remaining institutions of the welfare state, potentially undermining collective action toward more robust public services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".