Theorizing Praxis in Citizenship Learning: Civic Engagement and the Democratic Management of Inequality in AmeriCorps
Bibliographic record
Abstract
Over the last twenty years, the academic work on citizenship education and democracy promotion has grown exponentially. This research investigates the United States federal government’s cultivation of a ‘politics of citizenship’ through the Corporation for National and Community Service and the AmeriCorps program. Drawing on Marxist-feminist theory and institutional ethnography, this research examines the ways in which democratic learning is organized within the AmeriCorps program through the category of ‘civic engagement’ and under the auspices of federal regulations that coordinate the practice of AmeriCorps programs trans-locally. \nThe findings from this research demonstrate that the federal regulations of the AmeriCorps program mandate a practice and create an environment in which ‘politics,’ understood broadly as having both partisan and non-partisan dimensions, are actively avoided in formalized learning activities within the program. The effect of these regulations is to create an ideological environment in which learning is separated from experience and social problems are disconnected from the political and material relations in which they are constituted. Further, the AmeriCorps program cultivates an institutional discourse in which good citizenship is equated with participation at the local scale, which pivots on a notion of community service that is actively disengaged from the State. \nThrough its reliance on these forms of democratic consciousness, the AmeriCorps program engages in reproductive praxis, ultimately reproducing already existing inequalities within U.S. society. The primary elements of this reproductive praxis have been identified as ‘a local fetish’ and the ‘democratic management of inequality.’ The local fetish refers to the solidification of the local as the preferential terrain of democratic engagement and is characterized by an emphasis on face-to-face moral relationships, local community building, and small-scale politics. The democratic management of inequality refers to the development of discursive practices and the organization of volunteer labor in the service of poverty amelioration, which is in turn labeled ‘good citizenship.’ This research directs our attention to a more complicated notion of praxis and its relationship to the reproduction of social relations. Also, this research brings into focus the problem of the conceptualization of civil society and its relationship to democracy and capitalism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".