Introduction: trust, social cohesion, and integration
Bibliographic record
Abstract
Integrated societies are typically said to be those where citizens engage with each other across racial, ethnic, and class lines, and where citizens have roughly equal expectations and likelihood of being successful in attaining the goods that society has to offer on fair terms. But the precise connection between trust and integration is only implied: sometimes, it appears, integration is regarded as a precondition for the development of trust and social cohesion in diverse societies, and at other times as its result. In this Special Issue, we target these specific lacunae, with contributions that address what social trust is and what role it can play in producing integration or a sense of belonging in democratic societies, and others which examine the sources of ongoing distrust in democratic societies that render a full integration difficult, if not impossible, under current circumstances. Taken together, the thesis of the contributions to this Special Issue can be understood as follows: while trust matters to democratic society, and conduces to the integration of its populace, more attention must be paid to the sources of distrust, and whether distrust can be undermined in ways that enable more and better integration in the future. In this introduction, we situate the contributions to the Special Issue inside of existing literature on, first, trust, and then second, on distrust.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".