Jackets and Jewellery: Racialised Dispossession and Struggles over Public Space in Denmark
Bibliographic record
Abstract
Introduction During the late summer and fall of 2020, the Danish Prime Minister, Mette Frederiksen, repeatedly claimed that groups of so-called ‘ indvandrerdrenge ’ (immigrant boys) were creating public insecurity. According to Frederiksen, ‘ indvandrerdrenge ’ were hanging out in groups along sidewalks, on public transportation and in neighbourhood parking lots and public squares, where their (supposedly) inappropriate behaviour was making others feel unsafe. In response, Frederiksen and her government proposed to provide the police with a new authority to break up these groups of ‘ indvandrerdrenge ’, fine them and confiscate their ‘expensive jackets, watches and mobile phones’ (Frederiksen, 2020, translation by author). In this chapter, I examine this imperative to remove so-called ‘ indvandrerdrenge ’ from public spaces and confiscate their personal belongings. The literal translation of the term ‘ indvandrerdrenge ’ in English is ‘immigrant boys’. Yet, within the contemporary Danish context, this term is far from neutral. As a compound term of ‘immigrant’ and ‘boy’, ‘ indvandrerdrenge ’ conjures a specific racialised demographic of male youth or young adults. Referring to this group as ‘boys’ relies on an unsubtle paternalism, framing this group as lacking adult characteristics. Furthermore, rather than immigrants per se, ‘ indvandrerdrenge ’ refers to young ‘non-western’ immigrants or young men whose parents or grandparents have immigrated from so-called ‘non-western’ countries. These associations are tied to growing attempts by the Danish state to collect data testifying to the existence of this new social category. Since 2002, the national statistical agency, Statistics Denmark, has recorded statistical information about indvandrer (immigrants) and efterkommer (their descendants) living in Denmark. Statistics Denmark classifies immigrants and their descendants according to two categories: ‘western’ and ‘non-western’. Western countries include the member states of the EU (including the UK), Andorra, Iceland, Liechtenstein, Monaco, Norway, San Marino, Switzerland, Vatican City, Canada, the United States, Australia and New Zealand. The rest of the world's 156 countries are defined as ‘non-western’ countries. This categorisation enables politicians, journalists and social scientists to refer to ‘non-western’ immigrants and their descendants – people from over 150 different countries – as ‘a somewhat monolithic object of governmental intervention and social scientific inquiry’ (Zhang, 2020).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".