Pathways of resilience: Predicting school engagement trajectories for South African adolescents living in a stressed environment
Bibliographic record
Abstract
School engagement is associated with the resilience of adolescents living in stressed environments in sub-Saharan Africa. Even so, there is scant understanding of the antecedents of African students’ school engagement. In response, this article reports the results of an exploratory study conducted in 2018 and 2020 with a sample of 172 adolescents (average age: 16.02 years; SD = 1.67) from a risk-exposed municipality in South Africa. Clustered school engagement trajectories were identified using a longitudinal variant of k-means based on affective, behavioural, and cognitive school engagement. Evolutionary classification trees were used to identify meaningful predictors of the identified trajectories. The results point to specific combinations of factors – i.e., student age, parental/caregiver warmth, school resource levels, teacher competence – that sustained low and high school engagement trajectories. These combinations direct the attention of school psychologists and other service providers to the multiple systems that matter in varying ways for the school engagement of African students. They also call for continued investigation of the resource combinations that are salient to student engagement across stressed environments in sub-Saharan Africa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".