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Record W4392927516 · doi:10.32920/25417387.v1

Exploring Tax Relief Programs: A Path for Rental Housing in Canada

2024· preprint· en· W4392927516 on OpenAlexaffabout
Saniel Chand

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsToronto Metropolitan UniversityUniversity of British Columbia
Fundersnot available
KeywordsLaggingRentingRental housingBusinessPublic housingGovernment (linguistics)Social securityPublic economicsLabour economicsEconomic growthEconomicsMarket economyPolitical science

Abstract

fetched live from OpenAlex

To date, Canada’s housing policies have been attuned to a housing system in which most Canadian households obtain their housing from the private market. A small portion of households live in non-market social housing, including public housing, nonprofit housing and non-profit co-operatives. This has resulted in a system that relies on market mechanisms to deliver housing, and therefore housing policies which primarily focus on home ownership, leaving renting households lagging behind in housing security. The federal government’s National Housing Strategy (NHS) provides limited progress to alleviate stress on low-income rental housing, and this research paper seeks to propose policy and tax tools to improve the progress on objectives of the NHS. Specifically, this research explores using tax relief programs to reposition NHS initiatives to provide housing with meaningful measures of affordability, and housing directed to low-income earners.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.834

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.102
GPT teacher head0.230
Teacher spread0.128 · 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 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 routes2
Has abstractyes

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