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Record W6925272582 · doi:10.17605/osf.io/vwneq

Indigenous Resentment and Housing

2024· other· en· W6925272582 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsnot available
Fundersnot available
KeywordsResentmentOpposition (politics)HarmPublic housingIndigenousEliteFeelingNIMBY

Abstract

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Canada is currently facing a housing affordability crisis. Housing prices continue to rise as demand outpaces supply (Boynton, 2022) and Scotia Bank’s Chief Economist has recently argued that a considerable problem standing in the way of improved housing affordability is local opposition to new housing development (Labine, 2022). Many Canadians express support for policies that encourage housing construction and increase affordability yet we know that this support weakens among those with high anti-immigrant sentiments and, in the US, those with strong feelings of racial resentment (Rivard et al., 2024). There is a growing literature examining the factors that increase support or opposition towards housing development in Canada and internationally (Doberstein et al., 2016; Handy et al., 2008; Hankinson 2018; Lewis and Baldassare, 2010; Wicki and Kaufmann, 2022) but the literature has yet to properly consider the role of racial resentment in housing attitudes and the relationship between public and elite attitudes on local housing policy (with the exception of Trounstine 2020, 2023). Research suggests that support for development can increase when respondents are made aware of the potential of public benefits (Doberstein et al., 2016). Yet opposition increases when respondents are told the development might harm their neighbourhood character, strain public services, make parking more difficult, when the proposed development is closer to respondent’s place of residence, and among those concerned about the “type of people” moving into the new development (Hankinson, 2018; Lewis, 2015; Whittemore and BenDor, 2019). In Canada, racial resentment—or feelings of resentment/antipathy/racism towards a visible minority group—is best characterized by Canadian-born non-Indigenous peoples’ feelings towards Indigenous peoples. Indeed, Indigenous resentment has been shown to be an important predictor of policy attitudes in Canada (Beauvais, 2022) and is also associated with greater opposition toward government spending on policies that are deemed to benefit Indigenous peoples (Beauvais and Stolle, 2022). In December 2019, 87% of Squamish nation members voted to approve the Sen̓áḵw development. The project is presently being built by a collaboration between the Squamish and Westbank, a commercial property developer. The Sen̓áḵw project is an ideal case to study the effects of support for development within a real-world context. Studies that look at support for a housing project generally rely on a hypothetical project, asking the survey respondent to “imagine a housing development was being proposed near you”. In contrast, the Sen̓áḵw is a real-world example of a large-scale, controversial housing project being led by an out-group—the Squamish nation who, despite being the first inhabitants of the land, are now an ethnic minority group in Vancouver and whose ethnic origins differ from the city’s majority population. This project will advance our knowledge of (i) racial resentment, (ii) opposition to new housing, and (iii) reconciliation between Canadians and Indigenous peoples more generally. Inspired by the Sen̓áḵw project, this paper looks at the extent to which opposition/support towards a hypothetical housing development is affected by whether the project is spearheaded by a local Indigenous nation and the extent to which Indigenous residents make up the share of the new residents.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.034
GPT teacher head0.321
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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 routes1
Has abstractyes

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