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Record W4323655107 · doi:10.1111/tesg.12549

Financializing Through Crisis? Student Housing and Studentification During the Covid‐19 Pandemic and Beyond

2023· article· en· W4323655107 on OpenAlexafffund
Nick Revington, Celia Benhocine

Bibliographic record

VenueTijdschrift voor Economische en Sociale Geografie · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsInstitut National de la Recherche Scientifique
FundersSocial Sciences and Humanities Research Council of CanadaInstitut national de la recherche scientifique
KeywordsFinancializationReal estateAsset (computer security)GoodwillCoronavirus disease 2019 (COVID-19)PandemicBusinessRevenueEconomic growthEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract The emergence of purpose‐built student accommodation (PBSA) as a ‘global’ asset class has physically and socially transformed university cities through ‘new‐build studentification’ implicated in the financialization of urban space. Yet, the COVID‐19 pandemic has exposed the risk inherent in this asset's reliance on a narrow submarket, as students' domestic and international mobilities were temporarily disrupted. We interrogate PBSA providers' response to the pandemic through the analysis of real estate consultancy reports, firms' annual reports and other investor‐facing documents, in Africa, Australia, Europe and North America, demonstrating how the financialization of this niche sector is sustained through crisis. Tactics include building goodwill to expand market share, temporarily reorienting towards domestic students and operational strategies to cut costs and increase revenues. Despite the sector's optimism, these approaches amplify existing trends of finance‐driven new‐build studentification in university cities, characterized by uneven development, the privatization of student housing and deepening class and age segregation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.286
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes2
Has abstractyes

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