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Record W6945317182 · doi:10.25316/ir-19884

A Case Study of the Federal Lands Initiative

2024· dissertation· en· W6945317182 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingContext (archaeology)PoliticsCorporate governanceOrder (exchange)Federalism

Abstract

fetched live from OpenAlex

The Federal Lands Initiative was created as a one time $200-million fund which supports the transfer of surplus federal lands and buildings to eligible proponents. The program makes these lands available at significantly discounted or no cost, in order for them to be developed or renovated for use as affordable housing. Being that these sites were historically used for various operations within the federal government, local municipalities typically have the areas zoned for institutional land uses and as such, rezoning the property is a common requirement. This can present significant challenges depending on the local context and politics as well as negative stereotypes surrounding affordable housing. Potential proponents of the program are also charged with producing premium housing outcomes in energy efficiency and accessibility while maintaining below market rates amidst economic pressures. This research uses the Federal Lands Initiative as an exploratory case study to investigate the interplay between municipal, provincial and federal governance structures. It examines the frameworks that affect the success of the program and it explores the political and structural pressures surrounding the development of affordable housing in Canada.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.234
Teacher spread0.220 · 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 designNot applicable
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 routes1
Has abstractyes

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