The Social Treatment of Ex-Dropouts Reenrolled in Secondary School in South Africa
Bibliographic record
Abstract
Dropout recovery and return to school is an education access priority for government in countries in both thewestern and non-western worlds. In a qualitative investigation involving a sample of dropouts who hadre-enrolled in secondary school in South Africa, this study explored antisocial aspects in their social experiencesat school. The aim was to explore the social treatment of ex-dropout who rerolled in secondary school anddiscuss ways to help them reintegrate in the school community. The study revealed that the major antisocialaspects in dropout experience were prejudice and social hostility, expressed through experiences of socialostracism, isolation, categorisation and rejection. This was motivated by a matrix of intersecting modern andtraditional forces. Relational and physical aggressions, which occurred in response to dropout out-grouplabelling and categorisation, were major factors in the social interactions. The evidence of hostility and reactionssubstantiated previous studies. The various implications of the findings for the school climate were highlighted.The study stressed that for dropouts to reintegrate, the entire school culture that condones social categorisation,relational or physical aggression against them, needs to be altered.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".