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Record W2741543361 · doi:10.1177/0308518x17723631

The state’s estate: Devaluing and revaluing ‘surplus’ public land in Canada

2017· article· en· W2741543361 on OpenAlexaffabout
Heather Whiteside

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDevaluationState (computer science)Public landReal estateBureaucracyScrutinyReal propertyPublic propertyEconomicsEstateBusinessPoliticsMarket economyFinanceProperty rightsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Since the mid-1990s, Canadian public real property (land, buildings, and equipment) has been subject to regular scrutiny through bureaucratic procedures aimed at ridding the state’s estate of all ‘surplus’ properties. Surplus is transferred to Canada Lands Company, a state owned enterprise charged with privatizing public land. Bureaucratic devaluation thus allows for subsequent revaluation through multiple forms of state-sponsored remediation: the physical, legal, and financial manipulation of public property by Canada Lands Company. Analyzing Canada Lands Company’s history, role, budgets, and activities, this article uncovers the particular dynamics of how Canadian public land is being privatized through devaluation and revaluation by the state. Two arguments of broader significance for literatures on the political economy of the state and public land frame the discussion: (1) Canada Lands Company’s activities speak to the important managerial role played by the (Canadian) state in the land dispossession process; and (2) Canada Lands Company’s treatment of surplus public land as a financial asset is a distinguishing feature of the Canadian public property management system, setting it apart from elsewhere.

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)0.001
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 score0.434
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.196
Teacher spread0.172 · 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

Citations39
Published2017
Admission routes2
Has abstractyes

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