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Record W4293571753 · doi:10.22230/cjc.2022v47n2a4185

Smart City Toronto: Extraction, Enclosure, Rentier Capitalism

2022· article· en· W4293571753 on OpenAlexvenueaboutno aff
Barbara Jenkins

Bibliographic record

VenueCanadian Journal of Communication · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsCapitalismPoliticsCorporate governanceEnclosureExpansionismDiversification (marketing strategy)Political economyPolitical scienceEconomic systemEconomySociologyEconomicsBusinessEngineeringLawManagement

Abstract

fetched live from OpenAlex

Background: The failure of Sidewalk Labs’ smart city initiative in Toronto provides insight into the imperatives and contradictions of platform capitalism. Analysis: A political economy analysis reveals why this Alphabet subsidiary sought to expand the project beyond the parameters of the initial plan, stepping into a vacuum created by neoliberal forms of governance. Ultimately, the company’s expansionism resulted in a political backlash that scuttled the plan. Conclusion and implications: This case study highlights the political contradictions of platform capitalism, emphasizing the diversification of corporate strategies characterized by the extraction of data, enclosure of platforms, and accumulation of rentier profits.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0090.015
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.224
Teacher spread0.201 · 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.

Study designQualitative
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

Citations2
Published2022
Admission routes2
Has abstractyes

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