Confronting Gentrification: Can Creative Interventions Help People Keep More than Just Their Homes?
Bibliographic record
Abstract
Gentrification is changing the landscape of many American cities. As land values rise, people may lose their homes, neighbors, and sites of significance, along with their sense of place, community, and history. There is a critical need to build and preserve affordable housing, yet housing alone will not address the more than material losses. What role can the arts play in sustaining place attachments, restoring relationships, and building place knowledge in gentrifying neighborhoods? This paper explores this question through a systematic review of current research. We identify four prominent alternative interventions in gentrifying neighborhoods—creative placemaking, public pedagogy, community organizing, and public science—and explicate strengths and limitations of each approach. We find the strongest interventions bridge approaches—engaging artists as/and researchers, educators, and community leaders—and mobilize residents as participants in knowledge/cultural production. We note that initiatives that provide short-term benefit may simultaneously make the neighborhood more desirable—and thus more vulnerable to gentrification—in the longer-term. Finally, given the dearth of research in this area, we conclude with recommendations for future research that attends to issues of equity, process as well as outcome, and longitudinal effects of more than material interventions in gentrifying neighborhoods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".