Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".