Citizenship, Solidarity, and the Common Good
Bibliographic record
Abstract
Despite the liberal democratic insistence on citizenship and solidarity, we see a sharp rise in divisive politics, aggressive posturing, and social and political fragmentation in many countries. Moreover, it has been argued that the commitments regarding social solidarity in democratic states have either not fully materialized or have been replaced by “mutual hatred and resentment” (Mishra 2017, 14) in the general populace. Addressing the above challenges necessitates a fresh reflection on democratic priorities and principles. A meaningful realization of liberal democratic citizenship and solidarity, I contend, requires an agile notion of the common good, encouraging citizens to come together in the pursuit of their collective goals and projects, making necessary accommodations for the welfare of not only their compatriots but also noncitizens, immigrants and marginalized individuals who inhabit the same social and cultural space. To the above end, I draw upon liberal universalism and egalitarianism, emphasizing the principles of equality and human dignity, to show that any formulation of the common good must be consistent with well-known democratic ideals. Accordingly, I suggest that the social and cultural commitments of democratic citizens should be reimagined to adjust to liberal values of citizenship, solidarity, and the common good.
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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 | 0.007 | 0.052 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".