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Record W1593484829

Multiculturalism in Multinational States: Lessons from Canada and South Africa

2012· article· en· W1593484829 on OpenAlexaffabout
Paloma Raggo, Audie Klotz

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsCarleton University
Fundersnot available
KeywordsMulticulturalismMinority rightsPolitical scienceScrutinyPoliticsPolitical economyIndigenousImmigrationMultinational corporationDiversity (politics)SociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Multiculturalism is coming under greater scrutiny in Europe, but advocates still hold up Canada as a model for managing social diversity, because of its official commitment remains strong and unparalleled. This recent debate reinvigorates persistent questions about how liberal democracies integrate diverse populations, balance individual freedoms, create a unifying national narrative, promote common civic values, and adapt to prevailing international norms. We argue that two types of political claims in liberal democracies ought to be more clearly distinguished: (1) how individual rights provide the parameters of competition between various sub-national minority claims for group rights and (2) how some advocates for group rights challenge this hierarchical privileging of individual rights. To illustrate, we juxtapose Canada and South Africa, two former British colonies with long-standing ethnic-national divisions between European settler populations, contentious histories between those settlers and the indigenous peoples, and continued reliance on increasingly diverse immigration. We conclude that both proponents of multiculturalism and its detractors should pay closer attention to illiberal aspects of group rights, and we challenge the idea that a liberal multicultural framework is indeed attainable.

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)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0210.006
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.235
Teacher spread0.223 · 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
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

Citations0
Published2012
Admission routes2
Has abstractyes

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