‘New’ Dutch Civic Integration: learning ‘Spontaneous Compliance’ to address inherent difference
Bibliographic record
Abstract
In January 2022 the new Dutch Civic Integration programme was launched together with promises of improvements it would bring in facilitating the ‘integration’ of newcomers to the Netherlands. This study presents a critical discourse analysis of texts intended for municipalities to take on their new coordinating role in this programme. The analysis aims to understand the discourse in the texts, which actors are mobilized by them, and the role these texts and these actors play in processes of governmental racialization. The analysis demonstrates shifting complex assemblages are brought into cascades of governance in which all actors are disciplined to accept the problem of integration as a problem of cultural difference and distance, and then furthermore disciplined to adopt new practices deemed necessary to identify and even ‘objectively’ measure the inherent traits contributing to this problematic. Lastly, the analysis displays that all actors are disciplined to accept the solution of ‘spontaneous compliance’; a series of practices and knowledges, which move the civic integration programme beyond an aim of responsibilization, into a programme of internalization, wherein newcomers are expected to own and address their problematic ‘nature’, making ‘modern’ values their own.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".