Meta Gentrification: The Gentrification Nexus in the Advent of Corporate Landlordism
Bibliographic record
Abstract
This dissertation explores the rise of corporate landlords taking hold of the Phoenix Metropolitan Area’s housing market. The dissertation explores the local and national historical settings that led way to the Phoenix Metropolitan Area becoming one of the largest markets in the country for corporate landlords. After exploring the history of the Phoenix Metropolitan Area and the rise of corporate landlordism, the dissertation creates a unique dataset that classifies millions of sales records from 2000-2020 in the Phoenix Metropolitan Area. From this dataset, spatial statistics and predictive modelling can be used to describe and predict where corporate landlords are purchasing housing units, explain how corporate landlords are exacerbating housing scarcity, and lend hand in answering who is being affected by investors commodifying houses. Corporate landlords have greatly shaped both rental and homebuying markets. Due to this, conceptualizations of displacement and gentrification appear to struggle with the broad reaching effects of corporate landlordism. Gentrification typically assumes that displacement occurs because of a wealthier, more privileged group moving into a disadvantaged or ethnic enclave. Through the creation of this granular dataset and spatial analysis, this dissertation finds that corporate landlords affect nearly all demographics evenly and have consumed nearly 25% of Phoenix’s housing units. A quarter of the Phoenix Metropolitan Area’s homes are now held by investors. Contemporary conceptions of gentrification are too myopic; with a broad-based housing crunch, it is likely that serialized displacement within a region is normalized and not only contained to ethnic enclaves. This dissertation argues that meta gentrification, the omnipresence of housing competition onset by corporate landlords, is leading to housing scarcity across entire 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".