(Dis)advantage and (Dis)engaged: Reflections from the First Year of Secondary School in Australia
Bibliographic record
Abstract
Adolescents continue to be at risk of disengaging from formal education, particularly in the transition year from primary to secondary schooling. This is a critical time in their education journey and can affect their ongoing academic performances. This paper reflects on the initial findings of a project to gauge students’ levels of engagement in the first year of secondary school (12-13 years of age). The project was undertaken with students in 4 schools in two Australian states, located in low socio economic areas. Approximately 80 students from the 4 schools were selected to participate in an intervention project with specific targeted activities that aimed to increase levels of engagement in schooling to eventually aid them to aspire to desired career choices. A mixed methods research approach enabled us to capture, analyse and report on the participating students’ perceptions in terms of their attitudes towards schooling, their academic performance, and selected aspects of school life. We also interviewed their parents and teachers about these topics. The results indicated that there were some changes in attitudes towards schooling for some individuals, but generally the majority of student engagement levels remained static or tended to be negative. This remains a cause of concern for educators who are trying to find ways to encourage students to be more engaged with formal education that supports their career aspirations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".