MétaCan
Menu
Back to cohort
Record W4394714150 · doi:10.5206/ijoh.2023.3.16805

The NB Housing Summit: Solutions to Promote Housing Affordability in the Mid-sized City of Saint John, New Brunswick

2024· article· en· W4394714150 on OpenAlexafffundvenueabout
Julia Woodhall‐Melnik, Tobin LeBlanc Haley, Chloé Reiser, Emily Nombro, Damian Collins, Cassandra Monette

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of AlbertaUniversity of New Brunswick
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsFondation de la recherche en santé du Nouveau-Brunswick
KeywordsEconomic rentSummitRentingRental housingSAINTGovernment (linguistics)Affordable housingInvestment (military)Economic growthPublic housingBusinessEconomicsPolitical scienceMarket economyGeographyPolitics

Abstract

fetched live from OpenAlex

Canada is in the midst of a national housing crisis that renders households vulnerable to unstable and unaffordable housing, and various forms of homelessness. New Brunswick is a largely rural Canadian province with three mid-sized cities that have lower housing values than larger Canadian centres. However, affordability is eroding in the province’s housing market, and the rental market offers few options for low-to-moderate-income renters. Simultaneously, visible homelessness and encampments are on the rise. Saint John, one of the mid-sized cities, is a relatively new destination for housing market investment and interprovincial migration, which is leading to higher rents and housing purchase prices. Through an analysis of World Café data collected with 85 individuals (representatives from government, people with lived experience of housing instability, non-profit agencies, housing developers, etc.) in Saint John, New Brunswick, this paper presents locally driven, on-the-ground solutions to the housing and homelessness crises in a mid-sized city.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.408
Teacher spread0.340 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes4
Has abstractyes

Explore more

Same venueInternational Journal on HomelessnessSame topicHomelessness and Social IssuesFrench-language works237,207