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Record W4396668038 · doi:10.32920/25758315.v1

The Time is Ripe: New Financial Tools for the City of Toronto’s Parkland Dedication Rate

2024· preprint· en· W4396668038 on OpenAlexaffabout
Jean-François Obregón Murillo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsToronto Metropolitan UniversityToronto Public HealthParks Canada
Fundersnot available
KeywordsCashFinanceBusinessBiodiversityGeography

Abstract

fetched live from OpenAlex

Developments in Ontario municipalities convey parkland under Section 42 of the Planning Act. Cash-in-lieu is contributed for parkland acquisitions when parkland cannot be conveyed. The City of Toronto had amassed $237,620,212 in cash-in-lieu reserve funds as of December 31, 2019. Complex spending rules hamper the use of existing funds. Municipal officials estimate that by 2034 there will be 25 m2 of parkland per person in Toronto compared with 28 m2 in 2016. Washington, D.C. has a green space standard of 38 m2 per person. The Nature Conservancy of Canada and Rally Assets estimates an annual national biodiversity funding gap of CAD 19.5-26 billion. The use of new financial tools for biodiversity conservation is catching on in the financial and philanthropic sectors. This MRP recommends amending existing rules to facilitate the use of cash-in-lieu reserve funds, to pursue new financial tools to acquire parkland, and to convene stakeholders across sectors.

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)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.156
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0090.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0450.005

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 head0.112
GPT teacher head0.242
Teacher spread0.130 · 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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