Translating the nation through the sustainable, liveable city: The role of social media intermediaries in immigrant integration in Copenhagen
Bibliographic record
Abstract
This article explores settled Western migrants whose digital content provides recent, mostly Western migrants in Copenhagen with local know-how and city-related information. This new type of informal integration intermediary functions as an emerging digital component of wider urban integration industries that assist migrants with settlement and social integration. We draw on the sociological theory of translation as a social, productive practice that constructs new meanings through selective interpretations and conceptualise the work of these bloggers as translation. Relying on the analysis of their blog and Instagram posts, and on interviews, this article shows how their translations of the city, and through it Danishness, play a critical role in mediating narratives of ‘becoming local’. Despite the differences between the bloggers’ respective translations (including those afforded through blogs vs Instagram) and despite criticism of a lack of inclusion of the socio-cultural differences in Denmark, these intermediaries ultimately reinforce for newcomers the expectations of the ‘green-city citizen’ and integration into Danish culture and lifestyle. We argue that what makes their translations resonate is not only that social media itself allows them to perform their having become (almost) local, but also that they carefully use their personal reflections as migrants. At the same time, the fact that their personal experiences of the city have been shaped by their positionality as white migrants feeling very welcomed, and even passing for locals, in the city curtails these bloggers’ wider potential as informal intermediaries filling a gap within Copenhagen’s urban integration industries.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".