Decolonising civic integration: a critical analysis of texts used in Dutch civic integration programmes
Bibliographic record
Abstract
European civic integration programmes claim to provide newcomers with necessary tools for successful participation. Simultaneously, these programmes have been criticised for being restrictive, market-driven and for working towards an implicit goal of limiting migration. Authors have questioned how these programmes discursively construct an offensive image of the Other and how colonial histories are reproduced in the constructions seen today. The Dutch civic integration programme is considered a leading example of a restrictive programme within Europe. Research has critically questioned the discourses within its policies, yet limited research has moved beyond policy to focus on discourse in texts in practice. This study presents a critical discourse analysis of texts used in the civic integration programme and demonstrates that they participate in multiple discursive constructions: the construction of the Dutch nation-state and its citizens as inherently modern, the construction of the Other as Unmodern and thus a threat, and the construction of the hierarchical relationship between the two. The civic integration programme has been left out of discussions on decolonisation to date, contributing to it remaining a core practice of othering. This study applies post-colonial theories to understand the impacts of current discourse, and forwards possibilities for consideration of decolonised alternatives.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".