Bibliographic record
Abstract
This article argues that widespread racism and narratives about who constitutes the cultural ‘other’ undermine efforts to establish social trust through cultural integration. The article criticizes liberal nationalist models of trust and cultural integration which assume that the main factor that drives rising distrust in diverse societies is objective cultural distance. It holds that this narrative about the liberal’s dilemma unwittingly reproduces, or at least does not challenge, the very notion on which cultural racism is founded. The article discusses four ways in which this is problematic: First, it will lead to an incorrect model of how trust, diversity, and cultural integration are connected. Second, the neglect of racism deprives us of an important analytic tool for differentiating between justified and unjustified integration demands and thus makes it more likely that unjust burdens are imposed on immigrants. Third, a too strong or exclusive focus on cultural integration might undermine equality of opportunity by blaming distrust and lack of positive cooperation on cultural differences instead of racist prejudice. Fourth, it detracts from how power inequalities are bound up with racist narratives and boundary-making that leads to mistrust. Addressing racism in the integration context thereby is as much a matter of justice as it is about developing a model that can successfully address the liberal’s dilemma.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| 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 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".