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Record W4400773937 · doi:10.5206/ijoh.2023.3.16837

New Roles Amidst Crisis: Comparing Municipal Affordable Housing Strategies in New Brunswick

2024· article· en· W4400773937 on OpenAlexafffundvenueabout
Tobin LeBlanc Haley, Julia Woodhall‐Melnik, Laura Pin, Sarah Durelle

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsWilfrid Laurier UniversityUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsAffordable housingPolitical sciencePublic administrationBusinessEconomic growthEconomics

Abstract

fetched live from OpenAlex

Affordable Housing in New Brunswick is desperately needed. New Brunswickers have faced major challenges since the start of the 2019 Coronavirus (COVID-19) pandemic, including record-breaking rent hikes, extreme increases to the public housing waitlist, full shelters, and growing encampments. Despite this crisis, the provincial government has rolled out only modest improvements in tenant protections and social housing investments. At the core of the province’s approach to the rental housing crisis is a decidedly neoliberal focus on increasing supply through tax cuts and encouraging housing starts. At the same time, the government of New Brunswick has embarked on municipal reform that gives cities additional capacities, including the ability to mobilize inclusionary zoning by-laws. In response to the housing crisis and against the backdrop of municipal reform, municipal governments across the province have been rolling out new affordable housing strategies. This paper analyzes the most recent strategies in two of New Brunswick’s mid-sized cities: Fredericton, the capital city, and Saint John, a port city and an industrial hub of the province. These strategies have, notably, been developed in a period of dramatic municipal reform and crisis. Attending to the distinct policy mechanisms proposed in these different strategies, we ask four key questions: How is the affordable housing crisis characterized within the municipal strategies? What roles are identified for municipal governments? What solutions are proposed? How do these strategies differ? This comparative analysis will provide a much-needed interrogation of the ways that the two municipal housing strategies have been developed amidst an aggressive neoliberal policymaking campaign in the affordable housing arena on the part of the province. It will also add to the literature a needed focus on the Maritime region, which is often neglected despite facing some of the highest rent increases in the country. At the same time, the government of New Brunswick has embarked on municipal reform that gives cities additional taxation capacities and the ability to mobilize inclusionary zoning by-laws (GNB, n.d., Community Planning Act, 2017). In response to the housing crisis and against the backdrop of municipal reform, municipal governments across the province have been rolling out new affordable housing strategies. This paper analyzes the most recent strategies in two of New Brunswick’s mid-sized cities: Fredericton, the capital city, and Saint John, a port city and an industrial hub of the province. These strategies have, notably, been developed in a period of dramatic municipal reform and crisis. Attending to the distinct policy mechanisms proposed in these different strategies, we ask three key questions: How do the municipalities understand the affordable housing crisis? What solutions are proposed? How and why do these strategies differ? This comparative analysis will provide a much-needed interrogation of how these two municipalities are incorporating and/or resisting what has been an aggressive neoliberal policymaking campaign in the affordable housing arena on the part of the province. It will also add to the literature a needed focus on the Maritime region which is often neglected.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
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 head0.037
GPT teacher head0.355
Teacher spread0.318 · 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 designTheoretical or conceptual
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
Published2024
Admission routes4
Has abstractyes

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