Associations between social factors and school belonging among newcomer and non-newcomer youth in Sweden
Bibliographic record
Abstract
Feeling a sense of belonging at school is associated with important positive outcomes for youth and requires youth to engage in positive social relationships. Yet there is a limited understanding of the social factors most associated with youths' school belonging and limited evidence about whether correlates of school belonging vary for marginalized groups like newcomers compared to majority groups. Sweden provides an important context for investigation of these issues because, over the past two decades, the country has experienced an influx of asylum seekers and educational reforms that have altered the composition and functioning of Swedish secondary schools. This study addresses these gaps by (1) investigating which of eight social factors are associated with school belonging among diverse Swedish youth, and (2) examining whether newcomer status moderates the relationship between social factors and school belonging. Hierarchical regression and moderation analyses were used to analyze data from 14 to 19 year-old (n = 233) newcomers and non-newcomers in Sweden. An exploratory factor analysis revealed that the school belonging measure contained two factors: positive perceptions and negative perceptions (reverse coded). For both, stronger school belonging was associated with lower perceived ethnic discrimination. Positive perceptions of school belonging were also associated with more prosocial behaviours and lower emotional problems. Negative perceptions of school belonging were associated with more peer problems. Notably, quantity and quality of peer relationships were not associated with school belonging. There was no consistent evidence of newcomer status moderating the relationship between social factors and school belonging. These results highlight factors associated with school belonging which are modifiable and amenable to intervention or impact by policy-ethnic discrimination, prosocial behaviour, and emotional and peer problems. The absence of moderation by newcomer status suggests that school belonging interventions or related policies are likely to affect newcomer and non-newcomer students similarly.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".