MétaCan
Menu
Back to cohort
Record W7132993298

A Nation's Dilemma: Party Politics and the Production of Nationhood, Belonging and Citizenship in France's Face Veil Debate

2016· dissertation· W7132993298 on OpenAlexaff
Emily Jane Laxer

Bibliographic record

VenueTSpace · 2016
Typedissertation
Language
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsCitizenshipPublic sphereFace (sociological concept)IslamGovernment (linguistics)Immigration
DOInot available

Abstract

fetched live from OpenAlex

In April 2011 – following a two-year-long nationwide debate over Islamic veiling – the French government implemented a law that prohibits facial coverings in all public spaces. Prior research attributes this and other restrictive laws to France’s republican secular tradition. This dissertation takes a different approach. Building on literature that sees electoral politics as a site for generating – rather than merely reflecting – societal meanings, it argues that the 2011 ban arose in significant part out of political parties’ struggle to demarcate the boundaries of the electoral sphere in the face of an ultra-right electoral threat. Specifically, it shows that in seeking to prevent the ultra-right National Front party from monopolizing the religious signs issue, France’s major right and left parties agreed to portray republicanism as requiring the exclusion of face veiling from public space. Because it was forged in conflict, however, the agreement thus generated is highly fractured and unstable. It also conceals ongoing conflict, both within political parties and in civil society, over the precise meaning of French republicanism. The findings thus underscore the relationship between boundary drawing in the political sphere and the process of demarcating the cultural and political boundaries of nationhood, belonging and citizenship in contexts of immigrant diversity.

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)0.005
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.034
Scholarly communication0.0140.005
Open science0.0010.005
Research integrity0.0040.004
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 head0.026
GPT teacher head0.351
Teacher spread0.324 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicMigration, Refugees, and IntegrationFrench-language works237,207