Migrants as sustainability actors: Contrasting nation, city and migrant discourses and actions
Bibliographic record
Abstract
• Migrants are sustainability actors in their urban destinations. • Sustainability policy overlook the transformative role of migrants. • Technocratic and assimilationist notions dominate sustainability policy discourse. • Sustainability policy should embrace migration and plurality. Although it is widely recognized that migration is socially transformative, the potential contributions of migrants to transformations towards sustainability in their destination areas are often overlooked in mainstream discourse on environmentalism and sustainability. Here we seek to identify current narratives of migrants and sustainability across individual, urban, and national scales. Migrants are commonly framed in public policy as having no or even negative impacts on sustainability. The study hypotheses that the lived experience of sustainability by migrants within urban destinations differ from dominant discourses and perceptions of migrant populations within societies. We test and document such divergence using data from 21 interviews with key stakeholders from the city and Swedish national level, an attitudinal survey of 895 migrants and non-migrants in Malmö, Sweden; and a media analysis of local and national Swedish newspapers. Survey results show that migrants engage more extensively with a number of sustainability actions compared to non-migrants culminating in new insights on ‘migrants as sustainability actors’. By contrasting individual scale practices against urban to national sustainability narratives, the study illuminates current barriers to and the potential of migrants to play a transformative role in progress towards sustainability that is unrecognized in dominant policy discourses. To tap into this potential, the study emphasizes that sustainability policy across scales should embrace plurality and migration as fundamental parts of progress towards sustainability.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".