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Record W4281562416 · doi:10.32920/19773031

A new funding frontier evaluating the new community benefits charge legislation in Ontario

2022· preprint· en· W4281562416 on OpenAlexaboutno aff
Vanessa Opassinis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationStakeholderTransparency (behavior)FrontierBusinessConfusionPublic administrationFinanceAccountingPublic economicsPolitical scienceEconomicsPublic relations

Abstract

fetched live from OpenAlex

<p>Community Benefits Charges represent a new era of municipal finance and community benefit funding in Ontario, replacing a fraught and controversial use of Section 37 (density bonusing) and Section 42 (parkland dedication) of the Planning Act. This report undertook a critical policy analysis to identify early issues and how the legislation and regulations may be reformed to ensure it’s implementation is equitable, efficient and effective, and meets objectives of the province, municipalities and development community. Through an extensive literature review, stakeholder and policy analysis this report identified three key issues, including, stakeholder confusion due to a lack of transparency on the process to develop the charge, financial uncertainty for municipal and private sector projects due to projecting land values in the calculation of the charge and uneven impact on different municipalities and development types. This report recommends the removal of the land value basis, allowing greater variations in implementation and rigid strategy analysis to allow the CBC to better meet the intent of the Province and satisfy identified concerns of all stakeholders.</p> <p>Key Words: community benefits, density bonusing, development charges, land value capture,</p> <p>parkland dedication, municipal finance, affordability</p>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0150.000

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 head0.274
GPT teacher head0.326
Teacher spread0.052 · 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 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
Published2022
Admission routes1
Has abstractyes

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