MétaCan
Menu
Back to cohort

Multiculturalism without Citizenship?

2018· book· en· W2804980514 on OpenAlexaboutno aff
Will Kymlicka

Bibliographic record

VenueEdinburgh University Press eBooks · 2018
Typebook
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipMulticulturalismPoliticsImmigrationPolitical scienceTyingGender studiesSociologyPolitical economyLaw

Abstract

fetched live from OpenAlex

The model of multiculturalism that emerged in Canada in the 1970s was intimately linked to national citizenship. Multiculturalism was premised on the assumption that immigrants would settle permanently and become citizens, and multiculturalism was seen as an attribute of Canadian citizenship, and a way of enacting citizenship. This tie to citizenship arguably served the interests of both immigrants and the native-born majority. For immigrants, it ensured that multiculturalism did not become a pretext for social exclusion and political marginalization; and for the native-born majority, it helped ensure that multiculturalism was domesticated, as it were, tying recognition of diversity to a shared social and political order. But this model has faced two major challenges in recent years: a neoliberal challenge, which sought to reorient multiculturalism more towards market principles than citizenship principles; and a mobility challenge, which sought to reorient multiculturalism away from ideas of permanent settlement and national citizenship towards ideas of temporary migration and liquid mobility. I critically evaluate these two challenges, focusing in particular on how they understand horizontal relations amongst residents/citizens and vertical relations between residents/citizens and the state. I identify some surprising parallels in the two critiques, and suggest that neither offers a compelling alternative to multicultural national citizenship.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
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
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.262
Teacher spread0.231 · 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
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEdinburgh University Press eBooksSame topicMigration, Refugees, and IntegrationFrench-language works237,207