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Record W2158121927 · doi:10.1017/s000842390525998x

From Subjects to Citizens: A Hundred Years of Citizenship in Australia and Canada

2005· article· en· W2158121927 on OpenAlexaffabout
Campbell Sharman

Bibliographic record

VenueCanadian Journal of Political Science · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCitizenshipPoliticsState (computer science)MainstreamGovernment (linguistics)Political scienceMedia studiesLawGender studiesSociology

Abstract

fetched live from OpenAlex

From Subjects to Citizens: A Hundred Years of Citizenship in Australia and Canada , Pierre Boyer, Linda Cardinal and David Headon, eds., Ottawa: University of Ottawa Press, 2004, pp. xvi, 328. Citizenship is no longer viewed as a concept restricted to the relationship between an individual and the state. It is now seen as encompassing the series of overlapping identities that define an individual's relationship to a political community. This means that discussions of citizenship are now likely to involve an examination of individual and group rights, political participation and an individual's sense of belonging. The collection of conferences papers under review pushes this expanded notion of citizenship to its limits, and beyond. While the nominal topic is “a hundred years of citizenship in Australia and Canada,” the contents of the book include chapters on the Australian exploration of Antarctica, Nellie Melba as a famous Australian, the secret ballot and the franchise in Australia, government sponsorship of culture in Canada, and a fascinating (if depressing) study of evidentiary law relating to rape in two early twentieth-century cases. In addition to the startling range of topics, the styles of analysis and disciplinary backgrounds of the contributions vary widely, from literary to mainstream social science. A few chapters are more notable for their polemical approach than their content.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.302
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2005
Admission routes2
Has abstractyes

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