Democratic Education in the Literature Classroom: Integrating Political Literacy and Political Emotions into Agonistic Literary Discussions. A Response to “Agonism in a Classroom Discussion on Strindberg’s <em>Miss Julie</em>”
Bibliographic record
Abstract
In an era of rising polarization and populism, how can we transform the literature classroom into a site of democratic education? Drawing on agonistic scholarship, Tysklind et al. (2024) offer the agonistic literary discussion, a novel pedagogical approach aiming to prepare students for the complexities of democracy by forming collective identities and navigating conflictual consensus. To build on the authors’ work, this response article proposes two additions—political literacy and political emotions—and cautions against the risk of antagonism. Agonistic literary discussions can integrate political literacy through teaching relevant knowledge and careful questioning, enabling students to situate characters’ experiences in political contexts and identify power dynamics in texts and society. Political emotions can be infused through inductive discussions and the strategy of circulation, allowing students to grasp relations of power and invest collective identities on an emotional level. However, students risk antagonizing one another when they passionately discuss the political dimension of literary texts. Establishing hegemony and fostering forgiveness may be helpful strategies to mitigate this risk, provided they are applied in careful and power-conscious ways. Expanded in this fashion, agonistic literary discussions can more fully equip students to engage with the tumult of contemporary democracy.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".