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Record W2801674623 · doi:10.7939/r3jd4pw4b

Liberalism, Nationalism, and uses of the Word Citizenship: Canadian Discourses

2015· article· en· W2801674623 on OpenAlexaboutno aff
Patrick B McLane

Bibliographic record

VenueUniversity of Alberta Library · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismCitizenshipPolitical scienceLinguisticsWord (group theory)LiberalismSociologyGender studiesPoliticsLawPhilosophy

Abstract

fetched live from OpenAlex

The word citizenship is a keyword in many political debates, as well as legislation and public policy. Citizenship studies scholars debate the meaning, extent and effects of citizenship and these debates have intensified in recent years. This dissertation takes a different approach; it explores how the term citizenship is used in selected discourses. By treating citizenship as a word and examining its uses, rather than treating citizenship as a socially constructed being or concept, the following dissertation departs from much work in contemporary citizenship studies. While many scholars are engaged in debates over what citizenship is or should be, I will argue that if one accepts the precepts of these debates, one mistakenly attributes being to citizenship and thereby reinforce hegemonic uses of this word. To examine citizenship as a term in influential discourses, I begin with canonical texts of political theory before turning to uses of the word citizenship in selected Canadian discourses: for example, in discourses that speak of “Canadian citizenship,” or “Canada as a country of equal citizens.” Close readings of discourses that employ the words “citizenship” and “Canada” together reveal how citizenship is frequently enunciated as a political identity; as something a person can gain or be denied; and as related to national “sovereignty.” Within this context, the dissertation focuses on three key questions: 1) How do common uses of the term citizenship lead individuals to think about themselves and others as political actors? 2) How do the discourses examined justify the way the label “citizen” is assigned to some, but withheld from others? 3) How do the discourses examined relate “citizenship” to “nation” and to “sovereignty”? In responding to these questions a specific thesis will be defended; namely, that the discourses examined consistently posit that citizenship is an “artificial” creation (a product of social action, laws and policy), and that Canadian citizenship is often defined by contrasting it with “naturalized” forms of identity such as race and ethnicity. In making this argument, this dissertation makes a contribution to social and political thought by focusing critical analysis upon the notion that citizenship is an artificial being or construct. To repeat, from the perspective adopted in this dissertation, citizenship is just a word, and when we treat citizenship as artificial we mistakenly attribute existence to citizenship. Adopting the perspective that citizenship is just a word, rather than an artificial being, raises the possibility of attending to how this word is used to shape the way we think of ourselves and others, to introduce categorical divisions into human populations, to authorize distinct legal processes and entitlements for distinctly categorized persons (e.g. citizens and non-citizens), and to present fictions of well-ordered, even sovereign, nation-states. Indeed, the conclusion argues that treating citizenship as a word opens the possibility of asking why citizenship is a central term in contemporary political discourses and whether we really want it to be. Questioning the word citizenship and the consequences of its uses is important because doing so may foster political interventions that attend to what happens to bodies coded as non-citizens, to local communities as opposed to statist projects, and to material realities more than political artifices.

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)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.012
Science and technology studies0.0650.051
Scholarly communication0.0190.007
Open science0.0020.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.189
Teacher spread0.175 · 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 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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