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Record W4386562397 · doi:10.32920/24085116

Developer perceptions of inclusionary zoning

2023· preprint· en· W4386562397 on OpenAlexaffabout
Riley Malthaner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAffordable housingGeneral partnershipZoningPolitical scienceBusinessPublic administrationEconomic growthFinanceEconomics

Abstract

fetched live from OpenAlex

The City of Toronto is in the midst of an affordable housing crisis. Inclusionary Zoning (IZ) is a planning tool that has been heralded by many as a possible solution and has been widely used in the United States. This study aims to explore the implementation of the City of Toronto's IZ policy, specifically developer perceptions, to understand the potential impacts. IZ requires developers of new residential developments to set aside a portion of the building's total units to be sold or rented as affordable housing. The City of Toronto has opted to implement a mandatory policy with no incentives for developers. This decision has been received poorly by many in the development community and a recurring theme from the interview process is the need for a partnership model. As such, the City of Toronto should embrace and consider ways of supporting IZ outside of financial contributions. Key Words: inclusionary zoning, development, affordable housing, Toronto, rental, ownership, partnership

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)0.003
metaresearch head score (Gemma)0.010
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 score0.236
Threshold uncertainty score0.469

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.128
GPT teacher head0.297
Teacher spread0.169 · 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
Published2023
Admission routes2
Has abstractyes

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