Narratives of being and belonging from the perspectives of young Dutch-Muslims in The Netherlands
Bibliographic record
Abstract
This chapter looks at the ways Dutch-Muslim youth that come from families with migrant backgrounds give meaning to and position themselves within Dutch society. Originally written as an autoethnographic account for my master’s research paper, this chapter weaves together research participants’ life stories with my own experiences as an Indonesian Muslim woman, who at the time was a student that had been living in the Netherlands for 13 months, from August 2014 to September 2015. Through my research, I explore places that my participants identified as essential to their experiences growing up in the Netherlands. These explorations are unpacked through narratives of whiteness, neighbourhoods, and the complexity of religious identities. They offer a response to dominant integration discourses in the Netherlands, which, blended with security discourse, often depict young Dutch-Muslims with migrant backgrounds as a problem – a challenge to security and social integration – that needs to be solved. These depictions are gendered, reproducing orientalist notions of aggressive Muslim men and passive Muslim women. I argue that these narratives are integral, not external, to our understanding of Dutch society and represent a challenge to elite discourses that often generalise and misrepresent the plural identities of young Allochtoon Dutch-Muslims.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".