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Record W6986518841

Power and Control in Disinvested Affordable Housing: San Francisco’s Limited Equity Housing Co-operatives

2023· dissertation· en· W6986518841 on OpenAlexaboutno aff

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2023
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingEquity (law)TypologyPoliticsControl (management)Government (linguistics)Corporate governanceLocal government
DOInot available

Abstract

fetched live from OpenAlex

The promise of the co-operative housing typology extends beyond providing stable, affordable housing. Co-operatives strive to offer a resident-centered site of democratic participation, where ownership and limited equity combine to provide both collective and shareholder ownership of a valuable community asset. Contentiously, local governments and civic institutions seek certainty and control in housing, prioritizing technical expertise and institutional relationships over deeper investment in resident-owner capacity. Affordable housing practitioners face complex and politicized projects, where co-op health is often threatened by mistrust, institutional failures, and funding scarcity. In San Francisco, more than 2,000 limited equity housing co-operative units constitute a significant portion of the city’s legacy 1960s and 70s federally-funded housing stock. Co-ops routinely fall into crisis, where residents rely on dysfunctional boards, ill-suited housing management companies, and insufficient government support for their survival. Numerous co-ops face critical survival questions, including deferred maintenance and disrepair, potential redevelopment, political instability, and waning institutional support. This client-linked thesis delves into the landscape of one local government's relationship with its co-operative housing ecosystem. Through dozens of interviews, a literature review, policy analysis, and several case studies of existing co-ops, this thesis elucidates present-day challenges and findings, and by discussing peer-city case studies of Vancouver, Canada, and Washington, D.C., proposes viable solutions charting a path forward.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.012
GPT teacher head0.295
Teacher spread0.284 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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