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Organized Labor and Casino Politics in Toronto

2017· book-chapter· en· W2793164423 on OpenAlexaboutno aff
Steven Tufts

Bibliographic record

VenueCornell University Press eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownBattlePoliticsReal estateState (computer science)Government (linguistics)Labour economicsCapital (architecture)Power (physics)Political sciencePolitical economyEconomicsMarket economyFinanceLaw

Abstract

fetched live from OpenAlex

This chapter examines labor's role in a public battle over the siting of a casino in Toronto, which played out among different levels of government, real estate developers, casino corporations, and anti-casino community groups. While labor support for a new downtown casino was contingent and fragmented, it remains a fact that significant unions were strongly behind development, and even public sector unions that opposed it in the later stages of the debate had some contradictory interests. When capital and the state consider divisive development proposals, there is greater opportunity for actors such as labor and community groups to be deciding factors. Yet such opportunities to shape municipal politics and development still require discipline and solid strategy. It is not only the loss of casino and construction jobs for some unions that is relevant here, but also the failure of local organized labor to exercise power in a decisive manner at a moment when the opportunity presented itself.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.099
Threshold uncertainty score0.717

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0150.010
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.033
GPT teacher head0.244
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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