Bibliographic record
Abstract
This article sketches the dominant themes that have shaped Dutch discourse, policy and research on issues related to race, ethnicity, and immigration during the past 40 years. It will be shown that the paradigmatic foundations of Dutch minority research were laid in the 1980s and that mainstream research and discourse is largely about ethnic minorities; about their migration and their degree (or lack) of economic, social and political integration in the Netherlands. By (co)incident or design, ethnic minorities – invariably called allochtonen, a Dutch word for non-natives or aliens, irrespective of citizenship – are problematized, while mainstream research generally downplays the ramifications of the colonial history, and concomitant presuppositions of European (Dutch) cultural superiority. We present an extended discussion of the denial of racism and the de-legitimization of racism research. Common sense (notions of) racism profoundly shaped research interpretations and research agendas. Mainstream researchers and scholars are largely critical of antiimmigrant discourse, but with the silencing of race critical paradigms there are few concepts and frameworks left to analyze and contextualize which anti-immigrant sentiments and policies are historically rooted in the invention of race and the Other and which sentiments are fears, discomforts and insecurities resulting from the uncontrollable paradigms of globalization in a world that has become smaller.
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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".