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Record W2639437418 · doi:10.7202/1070610ar

The Political as Presence: On Agonism in Citizenship Education

2020· article· en· W2639437418 on OpenAlexvenueno aff
Ásgeir Tryggvason

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsAgonismCitizenshipDemocracySociologyAgonistic behaviourEpistemologyOrder (exchange)Political philosophyPolitical scienceLawSocial psychologyPsychologyPhilosophyEconomics

Abstract

fetched live from OpenAlex

In recent years, an agonistic approach to citizenship education has been put forward as a way of educating democratic citizens. Claudia W. Ruitenberg (2009) has developed such an approach and takes her starting point in Chantal Mouffe’s agonistic theory. Ruitenberg highlights how political emotions and political disputes can be seen as central for a vibrant democratic citizenship education. The aim of this paper is to critically explore and further develop the concepts of political emotions and political disputes as central components of an agonistic approach. In order to do this, I return to Mouffe’s point of departure in the concept of the political. By drawing on Michael Marder’s (2010) notion of enmity, I suggest how “the presence of the other” can be seen as a vital aspect of the political in citizenship education. By not abandoning the concept of enmity, and with the notion of presence in the foreground, I argue that Ruitenberg’s definition of political emotions needs to be formulated in a way that includes emotions revolving around one’s own existence as a political being. Moreover, I argue that in order to further develop the agonistic approach, the emphasis on the verbalization of opinions in political disputes needs to be relaxed, as it limits the political dimension in education and excludes crucial political practices, such as exodus.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.231
GPT teacher head0.457
Teacher spread0.226 · 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 designTheoretical or conceptual
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

Citations29
Published2020
Admission routes1
Has abstractyes

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