Pathways of resilience: Predicting school engagement trajectories for South African adolescents living in a stressed environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it