Individual, social, and family factors associated with high school dropout among low‐ <scp>SES</scp> youth: Differential effects as a function of immigrant status
Bibliographic record
Abstract
BACKGROUND: In most Western countries, the individual, social, and family characteristics associated with students' dropout in the general population are well documented. Yet, there is a lack of large-scale studies to establish whether these characteristics have the same influence for students with an immigrant background. AIMS: The first aim of this study was to assess the differences between first-, second-, and third-generation-plus students in terms of the individual, social, and family factors associated with school dropout. Next, we examined the differential associations between these individual, social, and family factors and high school dropout as a function of students' immigration status. SAMPLE: Participants were 2291 students (54.7% with an immigrant background) from ten low-SES schools in Montreal (Quebec, Canada). METHOD: Individual, social, and family predictors were self-reported by students in secondary one (mean age = 12.34 years), while school dropout status was obtained five or 6 years after students were expected to graduate. RESULTS: Results of logistic regressions with multiple group latent class models showed that first- and second-generation students faced more economic adversity than third-generation-plus students and that they differed from each other and with their native peers in terms of individual, social, and family risk factors. Moreover, 40% of the risk factors considered in this study were differentially associated with first-, second-, and third-generation-plus students' failure to graduate from high school. CONCLUSION: These results provide insights on immigrant and non-immigrant inner cities' students experiences related to school dropout. The implications of these findings are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".