The disorientation of democracy and civic life: (Neo)liberal democratic citizenship education in the twenty-first century
Bibliographic record
Abstract
In this article, we explore how liberal democracies are seeking to shape the civic engagement of their youngest citizens in the 2020s. To do so, we undertake a close investigation of the underlying assumptions attached to the “good citizen,” as represented in national, provincial, and state civics curricula in five Anglo-American liberal democracies: Canada, the USA, Australia, England, and New Zealand. Findings suggest that the market-oriented, enterprising, and entrepreneurial citizen continues to be enmeshed with efforts to detach democratic politics from collective, emancipatory, or solidarity-based action and deliberation, what Wendy Brown (Citation2019) has defined as the de-democratization of the political. Further, our findings demonstrate that civics curricula portray the twenty-first century “good citizen” as a homogeneous, unmarked subject; that is, the subject whose absence of identity markers places them implicitly within dominant social categories (e.g., white, male, middle class, straight, and able-bodied). We argue that in fact this subject is marked by dominant identities (e.g., white, heterosexual, Christian, etc.) which are used to generate an “other” towards whom the citizen is expected to offer respect and tolerance but not necessarily political inclusion.
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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 | 0.003 |
| 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 | 0.038 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".