The political sources of solidarity in diverse societies
Bibliographic record
Abstract
Building and sustaining solidarity is an enduring challenge in all liberal-democratic societies. Ensuring that individuals are willing to accept these “strains of commitment,” to borrow John Rawls’ apt phrase, has been a worry even in relatively homogeneous societies, and the challenge seems even greater in ethnically and religiously diverse societies. This paper focuses is on the political sources of solidarity. Much has been written about the economic and social factors that influence the willingness of the public to accept and support immigrants and minorities. But solidarity is also a political phenomenon, which can be built or eroded through politics. In addition, our focus on the political sources of solidarity. Understandably, the existing literature concentrates on the politics of backlash and exclusion. This paper looks at the politics of diversity from the opposite direction, asking what are the potential sources of political support for inclusion, and the conditions under which they are effective. How is solidarity built? How is it sustained? Reframing the analysis in this way does not necessarily produce optimism about the future prospects. But exploring the potential political sources of support leads to broader, multilayered perspective with long time horizons. The paper advances a framework for analysis which incorporates three levels: the sense of political community, the role of political agents, and impact of political institutions and policy regimes. Each of these levels, and the interactions among them, matter.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".