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Record W7060828594

Pension Fund City: Retirement, Real Estate, and Public Sector Labour

2022· dissertation· en· W7060828594 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2022
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAusterityRestructuringPublic sectorDeindustrializationPensionReal estateAsset (computer security)MarketizationSocial security
DOInot available

Abstract

fetched live from OpenAlex

In 1985, the Ontario Municipal Employees Retirement System and Ontario Teachers Pension Plan had asset values of around $10 billion apiece. Thirty-five years later, and both now hold portfolios of well over $100 billion. As major players in the global system of rentier capitalism, both have built up significant real estate portfolios, linking the retirements of their beneficiaries to a global housing crisis. At the same time as this has occurred, their beneficiaries – Ontario’s public sector workers – have come under increased pressure from the state as the targets of austerity and anti-welfare politics. Their pensions are invested in the same economic system that is targeting them for destruction. This thesis posits that this is not coincidental, but rather is the result of intertwined processes of welfare, labour, and economic restructuring under the umbrella of deindustrialization and neoliberalization. The marketization of pensions, the public-sectorization of the labour movement, and the hyper-commodification of housing are all linked together as part of capital’s conquest of social reproduction. I tell this story by linking Ontario’s pension reforms in the late-1980s to the attack on public sector labour and welfare waged by successive provincial and local governments into the present. I then try to consider what the options are for the labour movement to break this contradiction.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.279
Teacher spread0.239 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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