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Record W2584710048 · doi:10.36939/cjur/vol25no2/art43

Land Trusts and the Protection and Stewardship of Land in Canada: Exploring Non-Governmental Land Trust Practices and the Role of Urban Community Land Trusts

2016· article· en· W2584710048 on OpenAlexafffundvenueabout
Susannah Bunce, Farrah Chanda Aslam

Bibliographic record

VenueCanadian journal of urban research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoGovernment of Alberta
KeywordsStewardship (theology)Government (linguistics)Environmental planningWildernessLand managementEnvironmental resource managementLand useBusinessLand tenurePublic administrationGeographyPolitical scienceAgricultureEconomicsCivil engineeringLawPolitics

Abstract

fetched live from OpenAlex

This paper explores the mandates of non-government land trust organizations in Canada, the role of urban land in current land trust practices, and possibilities for the inclusion of land protection and stewardship in Canadian cities through a discussion of the community land trust (CLT) model. Through the creation of an inventory of Canadian non-governmental land trust organizations, we demonstrate that the majority of historical and contemporary land trust organizations focus on the protection and conservation of wilderness and rural lands, with limited focus on the protection and stewardship of existing urban lands. Additionally, we suggest that the CLT model, already in existence in several Canadian cities, offers a way to re-frame this emphasis and to encourage non-governmental and community-based urban land protection and stewardship in order to resist increasing land values and provide necessary community benefits that foster equitable access and affordability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.087
GPT teacher head0.251
Teacher spread0.164 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2016
Admission routes4
Has abstractyes

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