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Canada and the political geographies of rights

2001· article· en· W2022276788 on OpenAlexaffvenueabout
Nicholas Blomley, Geraldine Pratt

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsPoliticsContext (archaeology)GentrificationHuman rightsPolitical sciencePublic rightsSocial rightsMeaning (existential)SociologyPolitical geographyPolitical economyLawGeographyEconomic growth

Abstract

fetched live from OpenAlex

For some observers, liberal rights are politically disempowering, while for others they can provide a basis for mobilization, resistance and the formation of counter‐publics. Yet neither of these claims says much about the geography of rights, which provides the focus for our discussion. Rights are geographical in several senses: rights are often about access to space or place; in liberal societies, geographies of private and public shape access to rights; space naturalizes social relations; the politics of scale open up new debates about and strategies for attaining rights within and beyond Canada; and places are both defined and called upon in struggles over rights. In an exploration of two Canadian case studies ‐ gentrification in Vancouver and the status of Filipina domestic workers ‐ we examine the ways in which the geography of rights proves consequential to dominant and oppositional rights claiming. We briefly lay out the meaning and significance of rights, before a discussion of their political significance in the Canadian context.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.851

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.009
Science and technology studies0.0210.030
Scholarly communication0.0120.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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 designTheoretical or conceptual
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

Citations59
Published2001
Admission routes3
Has abstractyes

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