Bibliographic record
Abstract
Banning minarets by referendum in Switzerland, publicly burning Korans in the United States, prohibiting kirpans in public spaces in Canada—these are all examples of the rising backlash against diversity that is spreading across multicultural societies. Trust has always been precarious, and never more so than as a result of increased immigration. The number of religions, races, ethnicities, and cultures living together in democratic communities and governed by shared political institutions is rising. The failure to construct public policy to cope with this diversity—to ensure that trust can withstand the pressure that diversity can pose—is a failure of democracy. The threat to trust originates in the perception that the values and norms that should underpin a public culture are no longer truly shared. Therefore, societies must focus on building trust through a revitalized public culture. In Trust, Democracy, and Multicultural Challenges , Patti Tamara Lenard plots a course for this revitalization. She argues that trust is at the center of effective democratic politics, that increasing ethnocultural diversity as a result of immigration may generate distrust, and therefore that democratic communities must work to generate the conditions under which trust between newcomers and “native” citizens can be built, so that the quality of democracy is sustained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".